Figures_MSA-PD
Rasmus Rydbirk
03-12-2024
Setup
library(conos)
library(magrittr)
library(dplyr)
library(cacoa) # github.com/kharchenkolab/cacoa## Warning: replacing previous import 'ape::where' by 'dplyr::where' when loading
## 'cacoa'library(sccore)
library(scHelper) # github.com/rrydbirk/scHelper
library(qs)
library(ggplot2)
library(ggsci)
library(cowplot)
library(ggpubr)
library(STRINGdb)
library(reshape2)
library(ggforce)
library(ggpmisc)
library(RColorBrewer)
library(CRMetrics)
library(ggrepel)
library(circlize)
library(ComplexHeatmap)
library(stringr)
library(rstatix)
library(slingshot)
## Comparisons
comp <- list(c("CTRL","MSA"),
c("CTRL","PD"),
c("MSA","PD"))
comp.long <- list(c("CTRL vs. MSA","CTRL vs. PD"),
c("CTRL vs. MSA","PD vs. MSA"),
c("CTRL vs. PD","PD vs. MSA"))
# Palettes
## Major celltypes
anno.neurons <- qread("anno_neurons.qs")
anno.neurons.major <- anno.neurons %>%
collapseAnnotation("GABA") %>%
collapseAnnotation("GLU") %>%
renameAnnotation("GABA", "Inh. neurons") %>%
renameAnnotation("GLU", "Exc. neurons") %>%
collapseAnnotation("MSN")## Collapsing 11 labels containing 'GABA' in their name into one label.
## Collapsing 2 labels containing 'GLU' in their name into one label.
## Collapsing 3 labels containing 'MSN' in their name into one label.anno <- qread("anno_major.qs")
anno.major <- anno[!names(anno) %in% names(anno.neurons.major) & !anno == "Neurons"] %>%
{factor(c(., anno.neurons.major))}
# qsave(anno.major, "/work/msa/03_annotations/anno_0final.qs")
pal.major <- brewer.pal(n = 10, "Set1") %>%
c("lightblue3") %>%
setNames(levels(anno.major)[c(9,8,3,5,10,6:7,2,1,4)])## Warning in brewer.pal(n = 10, "Set1"): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Condition
pal.major %<>% c(c("yellow", brewer.pal(n = 6, "Accent")[5:6]) %>% setNames(c("CTRL","PD","MSA")))
## Comparisons
pal.major %<>% c(brewer.pal(n = 10, "Paired")[c(6,8,10)] %>% setNames(c("CTRL vs. MSA","CTRL vs. PD","PD vs. MSA")))
## Neurons
pal.major %<>% c(setNames(c("navy",
"mediumblue",
"lightslateblue",
"lightskyblue",
"lightblue",
"lightseagreen",
"blue",
"cadetblue1",
"cyan",
"cyan4",
"aquamarine2",
"lightcoral",
"brown1",
"orange",
"darkorange4",
"lightgoldenrod3"),
levels(anno.neurons)))
## Glia
anno.glia <- c("anno_astro.qs",
"anno_oligo.qs",
"anno_opc.qs") %>%
lapply(qread) %>%
Reduce(c, .) %>%
factor() %>%
renameAnnotation("Homeostatic_astrocytes", "AS_homeostatic") %>%
renameAnnotation("Reactive_astrocytes", "AS_reactive") %>%
renameAnnotation("Homeostatic_LINC01608", "OL_LINC01608") %>%
renameAnnotation("Homeostatic_SLC5A11", "OL_SLC5A11") %>%
renameAnnotation("Reactive_SCGZ", "OL_SGCZ")
pal.major %<>% c(setNames(c("pink",
pal.major["Astrocytes"],
pal.major["Oligodendrocytes"],
"chartreuse",
"darkolivegreen",
"brown4",
"coral"),
levels(anno.glia)))
## Microglia
anno.micro <- qread("anno_micro_pvm.qs") %>%
renameAnnotation("Steady-state","MIC_steady-state") %>%
renameAnnotation("Intermediate1","MIC_intermediate1") %>%
renameAnnotation("Intermediate2","MIC_intermediate2") %>%
renameAnnotation("Activated","MIC_activated")
pal.major %<>% c(setNames(c(pal.major["Microglia"],
"maroon4",
"magenta",
"pink3",
pal.major["PVMs"]),
levels(anno.micro)))
## Phago. assay
pal.major %<>% c(setNames(c("purple",
"black",
pal.major[c("CTRL","PD","MSA")]),
c("PBS",
"LPS",
"CTRL CSF",
"PD CSF",
"MSA CSF")))
## Houston assay
pal.major %<>% c(setNames(c(pal.major["PBS"],
pal.major["CTRL"],
"yellow3",
"orange",
pal.major["PD"],
"cyan4",
"navyblue",
pal.major["MSA"],
"pink3",
"red4"),
c("Microglia medium",
"CTRL CSF, no dil.",
"CTRL CSF, 1:2 dil.",
"CTRL CSF, 1:4 dil.",
"PD CSF, no dil.",
"PD CSF, 1:2 dil.",
"PD CSF, 1:4 dil.",
"MSA CSF, no dil.",
"MSA CSF, 1:2 dil.",
"MSA CSF, 1:4 dil.")))
# Load major Conos object
con <- qread("con_major.qs", nthreads = 10)
tt <- Sys.time()Define helper functions
getOntWithFamily <- function(cao, comp) {
fams <- cao$test.results[["GSEA"]]$families
df.all <- list(cao$.__enclos_env__$private$getOntologyPvalueResults(name = "GSEA", genes = "up", p.adj = 1, q.value = 1),
cao$.__enclos_env__$private$getOntologyPvalueResults(name = "GSEA", genes = "down", p.adj = 1, q.value = 1)) %>%
bind_rows() %>%
mutate(logp = -log10(p.adjust),
comp = comp)
df <- cacoa:::getOntologyFamilyChildren(df.all, fams=fams, type = "GSEA", subtype = "BP")
return(list(df.all = df.all,
df = df,
fams = fams))
}lianaCircos <- function(df,
top.interactions = 30,
text.size = 1,
pal = pal.major,
cell.types = c("Astrocytes",
"Immune",
"Microglia",
"Neurons",
"OPCs",
"Oligodendrocytes",
"PVMs",
"Pericytes/endothelial"),
big.gap = 5,
small.gap = 2,
arrow.width = 3,
link.ramp.rel = T,
link.sort = F,
scale = F,
arrow.head.width = 0.3,
arrow.head.length = 0.3,
link.ramp.col = c("navy", "grey", "firebrick")) {
input_df <- df %>%
slice_max(order_by = score, n = top.interactions) %>%
mutate(target = paste0(target, " ")) %>%
mutate(source_lig = paste0(source, "|", ligand),
target_rec = paste0(target, "|", receptor))
if (link.ramp.rel) {
arr_wd <- rep(arrow.width, nrow(input_df))
} else {
arr_wd <- (((input_df$score - min(input_df$score))/(max(input_df$score) - min(input_df$score))) * (arrow.width)) + 1
}
# Colors and segments
anno.col <- setNames(pal,
cell.types) %>%
c(., "Oligodendrocytes " = unname(.["Oligodendrocytes"]))
cell_cols <- anno.col[unique(c(unique(input_df$source), unique(input_df$target), "Oligodendrocytes "))]
link_cols <- c()
if (!link.ramp.rel) {
for (i in input_df$source_lig) {
link_cols <- c(link_cols, cell_cols[str_extract(i,
"[^|]+")])
}
} else {
input_df %<>%
arrange(score)
df.down <- input_df %>% filter(score <= 0)
link_down <- colorRampPalette(c(link.ramp.col[1], link.ramp.col[2]))(nrow(df.down))
df.up <- input_df %>% filter(score > 0)
link_up <- colorRampPalette(c(link.ramp.col[2], link.ramp.col[3]))(nrow(df.up))
link_cols <- c(link_down, link_up)
}
segments <- unique(c(paste0(input_df$source, "|", input_df$ligand),
paste0(input_df$target, "|", input_df$receptor)))
grp <- str_extract(segments, "[^|]+") %>%
setNames(segments)
# Redo colors
cell_cols2 <- grp
for (i in unique(grp)) {
cell_cols2[cell_cols2 == i] <- cell_cols[i]
}
# Plot
input_df %>%
select(source_lig, target_rec, score) %>%
chordDiagram(directional = 1,
group = grp,
scale = scale,
diffHeight = 0.005,
direction.type = c("arrows"),
link.arr.type = "triangle",
annotationTrack = c(),
preAllocateTracks = list(
list(track.height = 0.05),
list(track.height = 0.25),
list(track.height = 0.05)),
big.gap = big.gap,
transparency = 1,
link.arr.lwd = arr_wd,
link.arr.col = link_cols,
link.arr.length = arrow.head.length,
link.arr.width = arrow.head.width,
small.gap = small.gap
)
circos.track(track.index = 2, panel.fun = function(x, y) {
circos.text(CELL_META$xcenter,
CELL_META$ylim[1],
str_extract(CELL_META$sector.index, "[^|]+$"),
facing = "clockwise",
niceFacing = TRUE,
adj = c(0, 0.55),
cex = 1)
}, bg.border = NA)
# Split segments
for (l in segments) {
highlight.sector(l, track.index = 3, col = cell_cols2[l])
}
# Add ligand/receptor track
## Ligand
highlight.sector(input_df$source_lig,
track.index = 1,
col = "black",
text = "Ligands",
cex = 1,
text.col = "white",
niceFacing = TRUE)
## Receptor
highlight.sector(input_df$target_rec,
track.index = 1,
col = "white",
text = "Receptors",
cex = 1,
text.col = "black",
border = "black",
niceFacing = TRUE)
# Legends
minmax <- input_df %>%
pull(score) %>%
{pmax(abs(min(.)), max(.))} %>%
formatC(digits = 1) %>%
as.numeric()
col.range = c(-minmax, 0, minmax)
lgd_links = Legend(at = col.range,
col_fun = colorRamp2(col.range, link.ramp.col),
title_position = "topleft",
title = "Links")
lgd_ct <- Legend(labels = unique(c(input_df$source, input_df$target)),
title = "Cell type",
type = "points",
legend_gp = gpar(col = "transparent"),
background = cell_cols[unique(c(input_df$source, input_df$target))])
lgd_list_vertical = packLegend(lgd_ct, lgd_links)
draw(lgd_list_vertical,
just = c("left", "bottom"),
x = unit(5, "mm"),
y = unit(5, "mm"))
circos.clear()
}getTscanTrajectory <- function(con, anno) {
requireNamespace("TSCAN", quietly = T)
emb <- con$embedding[names(anno), ]
anno %<>% .[rownames(emb)]
cent.ids <- emb %>%
rownames() %>%
split(anno)
centroids <- cent.ids %>%
lapply(\(cid) emb[cid, ]) %>%
lapply(colMeans) %>%
bind_rows() %>%
t() %>%
`colnames<-`(c("UMAP1","UMAP2"))
mst <- centroids %>%
TSCAN::createClusterMST(clusters = NULL)
line.data <- TSCAN::reportEdges(centroids, mst = mst, clusters = NULL)
return(line.data)
}plotGenePseudoBulk <- function(gene, cm.pseudo, legend = T) {
idx <- cm.pseudo %>%
colnames() %>%
data.frame(id = .) %>%
mutate(condition = strsplit(id, "_|!!") %>% sget(1),
ct = strsplit(id, "!!") %>% sget(2)) %>%
mutate(ord = order(condition, ct))
x <- cm.pseudo %>%
.[match(gene, rownames(.)), match(colnames(na.omit(.)), colnames(.))] %>%
.[idx$ord]
plot.dat <- x %>%
{data.frame(sample = names(.),
value = unname(.))} %>%
mutate(anno = strsplit(sample, "!!") %>%
sget(2),
condition = strsplit(sample, "!!|_") %>%
sget(1)) %>%
mutate(anno = factor(anno))
stat.test <- plot.dat %>%
group_by(anno) %>%
rstatix::wilcox_test(value ~ condition) %>%
rstatix::add_xy_position(x = "anno", step.increase = 0.1)
p <- plot.dat %>%
ggplot(aes(anno, value)) +
geom_boxplot(aes(fill = condition)) +
theme_bw() +
theme(line = element_blank(),
axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1),
axis.ticks.x = element_line()) +
labs(x = "", y = "Normalized pseudobulk expression", fill = "", title = paste0(gene, " expression")) +
scale_fill_manual(values = ggsci::pal_jama()(3)) +
stat_compare_means(aes(fill = condition), method = "kruskal", label = "p.signif") +
stat_pvalue_manual(stat.test)
if (!legend) p <- p + guides(fill = "none")
return(p)
}Figure 1
Figure 1b
con$plotGraph(groups = anno.major,
embedding = "UMAP",
size = 0.1,
palette = pal.major,
font.size = 3,
raster = T,
show.labels = T,
plot.na = F,
show.legend = T,
legend.title = "Cell type",
alpha = 0.05) +
dotSize(3) +
labs(x="UMAP1", y= "UMAP2") +
theme(line = element_blank())
Figure 1c
c("RBFOX3","MOG","VCAN","AQP4","CSF1R","PDGFRB","PTPRC","MRC1","GAD1","SLC17A7","PPP1R1B") %>%
sort() %>%
lapply(function(g) con$plotGraph(embedding = "UMAP", gene=g, title=g, size=0.1, plot.na = F, palette = colorRampPalette(c("grey90","firebrick"))) + theme(line = element_blank())) %>%
cowplot::plot_grid(plotlist=., ncol=2)
Figure 1d
cm.merged <- con$getJointCountMatrix()
markers <- c("AQP4","PTPRC","CSF1R","RBFOX3","SLC17A7","GAD1","PPP1R1B","MOG","VCAN","PDGFRB","MRC1")
dotPlot(markers,
cm.merged,
anno.major %>%
factor(., levels = sort(levels(.))[c(1,3,5,2,4,6,7:10)]),
cols = c("white","firebrick"),
gene.order = markers)
Figure 2
Load data
cao_msa <- qread("cao_major_msa.qs", nthreads = 10)
cao_pd <- qread("cao_major_pd.qs", nthreads = 10)
cao_dis <- qread("cao_major_dis.qs", nthreads = 10)Figure 2a
spc <- con$getDatasetPerCell()
cpc <- spc %>%
as.character() %>%
grepl.replace(c("CTRL","MSA","PD")) %>%
`names<-`(names(spc))
con$plotGraph(groups = cpc,
embedding = "UMAP",
size = 0.1,
alpha = 0.05,
palette = pal.major,
font.size = 3,
raster = T,
show.labels = T,
plot.na = F,
mark.groups = F,
show.legend = T) +
labs(x="UMAP1", y= "UMAP2", col = "") +
theme(legend.position = "bottom",
line = element_blank()) +
dotSize(3)
Figure 2b
cao_msa$plotCellLoadings(show.pvals = F,
alpha = 0)$data %>%
ggplot(aes(ind, values, fill = ind)) +
geom_hline(yintercept = 0, col = "black") +
geom_violin() +
coord_flip() +
theme_bw() +
scale_fill_manual(values = pal.major) +
theme(legend.position = "none",
line = element_blank()) +
scale_y_continuous(breaks = c(-1,0,1),
limits = c(-1,1),
labels = c("-1\nCTRL",0,"1\nMSA")) +
labs(x = "", y = "")
Figure 2c
cao_pd$plotCellLoadings(show.pvals = F,
alpha = 0)$data %>%
ggplot(aes(ind, values, fill = ind)) +
geom_hline(yintercept = 0, col = "black") +
geom_violin() +
coord_flip() +
theme_bw() +
scale_fill_manual(values = pal.major) +
theme(legend.position = "none",
line = element_blank()) +
scale_y_continuous(breaks = c(-1,0,1),
limits = c(-1,1),
labels = c("-1\nCTRL",0,"1\nPD")) +
geom_vline(xintercept = 7.5, col = "red") +
labs(x = "", y = "")## Warning: Removed 15 rows containing non-finite outside the scale range
## (`stat_ydensity()`).
Figure 2d
cao_dis$plotCellLoadings(show.pvals = F,
alpha = 0)$data %>%
ggplot(aes(ind, values, fill = ind)) +
geom_hline(yintercept = 0, col = "black") +
geom_violin() +
coord_flip() +
theme_bw() +
scale_fill_manual(values = pal.major) +
theme(legend.position = "none",
line = element_blank()) +
scale_y_continuous(breaks = c(-1,0,1),
limits = c(-1,1),
labels = c("-1\nPD",0,"1\nMSA")) +
geom_vline(xintercept = 5.5, col = "red") +
labs(x = "", y = "")
Figure 2e
cao_msa$cell.groups.palette <- pal.major
cao_pd$cell.groups.palette <- pal.major
cao_dis$cell.groups.palette <- pal.major
cao_msa$plotExpressionShiftMagnitudes() + labs(y = "Normalized expression distance")
cao_pd$plotExpressionShiftMagnitudes() + labs(y = "Normalized expression distance")
cao_dis$plotExpressionShiftMagnitudes() + labs(y = "Normalized expression distance")## Notch went outside hinges
## ℹ Do you want `notch = FALSE`?
Figure 3
Load data
cao <- qread("cao_neurons.qs", nthreads = 10)
cao_msa <- qread("cao_neurons_msa.qs", nthreads = 10)
cao_pd <- qread("cao_neurons_pd.qs", nthreads = 10)
cao_dis <- qread("cao_neurons_dis.qs", nthreads = 10)Figure 3a
cao$plotEmbedding(groups = cao$cell.groups,
size = 0.1,
palette = pal.major,
font.size = 3,
raster = T,
show.labels = T,
plot.na = F,
alpha = 0.1) +
theme(line = element_blank()) +
labs(x="largeVis1", y= "largeVis2")
Figure 3b
cm.merged <- cao$data.object$getJointCountMatrix()
markers <- c("GAD1","GAD2","PPP1R1B","SLC17A7","CHAT","CALB2","DCC","ID2","MEIS2","ST18","NKX2-1","NR2F2","PAX6","PVALB","RXFP1","SST","VIP","RELN","SATB2","DRD1","DRD2","FOXP2", "LRP8", "VLDLR")
dotPlot(markers,
cm.merged,
anno.neurons,
cols = c("white","firebrick"),
gene.order = markers)
Figure 3c
cao_msa$plotCellLoadings(show.pvals = F,
alpha = 0)$data %>%
ggplot(aes(ind, values, fill = ind)) +
geom_hline(yintercept = 0, col = "black") +
geom_violin() +
coord_flip() +
theme_bw() +
scale_fill_manual(values = pal.major) +
theme(legend.position = "none",
line = element_blank()) +
scale_y_continuous(breaks = c(-1,0,1),
limits = c(-1,1),
labels = c("-1\nCTRL",0,"1\nMSA")) +
labs(x = "", y = "")
Figure 3d
cao_pd$plotCellLoadings(show.pvals = F,
alpha = 0)$data %>%
ggplot(aes(ind, values, fill = ind)) +
geom_hline(yintercept = 0, col = "black") +
geom_violin() +
coord_flip() +
theme_bw() +
scale_fill_manual(values = pal.major) +
theme(legend.position = "none",
line = element_blank()) +
scale_y_continuous(breaks = c(-1,0,1),
limits = c(-1,1),
labels = c("-1\nCTRL",0,"1\nPD")) +
geom_vline(xintercept = 13.5, col = "red") +
labs(x = "", y = "")
Figure 3e
cao_dis$plotCellLoadings(show.pvals = F,
alpha = 0)$data %>%
ggplot(aes(ind, values, fill = ind)) +
geom_hline(yintercept = 0, col = "black") +
geom_violin() +
coord_flip() +
theme_bw() +
scale_fill_manual(values = pal.major) +
theme(legend.position = "none",
line = element_blank()) +
scale_y_continuous(breaks = c(-1,0,1),
limits = c(-1,1),
labels = c("-1\nPD",0,"1\nMSA")) +
labs(x = "", y = "")
Figure 3f
sample.groups <- cao$sample.groups
cao$plotCellDensity(show.cell.groups = F)$CTRL +
geom_circle(aes(x0 = 0.75, y0 = -4.5, r = 1)) +
geom_circle(aes(x0 = 3.8, y0 = -1.1, r = 0.5), col = "cyan3") +
theme(line = element_blank())
# MSA
cao$sample.groups <- sample.groups %>%
names() %>%
grepl.replace(c("CTRL","MSA","PD")) %>%
`names<-`(sample.groups %>% names()) %>%
.[. %in% c("CTRL","MSA")]
cao$target.level <- "MSA"
cao$estimateCellDensity(method = "graph", name = "msa.density")
cao$estimateDiffCellDensity(type = "subtract", name = "msa.density")
cao$plotCellDensity(show.cell.groups = F, name = "msa.density")$MSA +
geom_circle(aes(x0 = 0.75, y0 = -4.5, r = 1)) +
geom_circle(aes(x0 = 3.8, y0 = -1.1, r = 0.5), col = "cyan3") +
theme(line = element_blank())
# PD
cao$sample.groups <- sample.groups %>%
names() %>%
grepl.replace(c("CTRL","MSA","PD")) %>%
`names<-`(sample.groups %>% names()) %>%
.[. %in% c("CTRL","PD")]
cao$target.level <- "PD"
cao$estimateCellDensity(method = "graph", name = "pd.density")
cao$estimateDiffCellDensity(type = "subtract", name = "pd.density")
cao$plotCellDensity(show.cell.groups = F, name = "pd.density")$PD +
geom_circle(aes(x0 = 0.75, y0 = -4.5, r = 1)) +
geom_circle(aes(x0 = 3.8, y0 = -1.1, r = 0.5), col = "cyan3") +
theme(line = element_blank())
Figure 3g
cao_msa$plotExpressionShiftMagnitudes() + labs(y = "Normalized expression distance")## Notch went outside hinges
## ℹ Do you want `notch = FALSE`?
cao_pd$plotExpressionShiftMagnitudes() + labs(y = "Normalized expression distance")## Notch went outside hinges
## ℹ Do you want `notch = FALSE`?
## Notch went outside hinges
## ℹ Do you want `notch = FALSE`?
cao_dis$plotExpressionShiftMagnitudes() + labs(y = "Normalized expression distance")## Notch went outside hinges
## ℹ Do you want `notch = FALSE`?
## Notch went outside hinges
## ℹ Do you want `notch = FALSE`?
## Notch went outside hinges
## ℹ Do you want `notch = FALSE`?
## Notch went outside hinges
## ℹ Do you want `notch = FALSE`?
## Notch went outside hinges
## ℹ Do you want `notch = FALSE`?
Figure 4
Load data
con.glia <- qread("/work/msa/02_data/conos/con_oligo_astro_opc.qs", nthreads = 10)
cao_msa <- qread("cao_oligo_astro_opc_msa.qs", nthreads = 10)
cao_pd <- qread("cao_oligo_astro_opc_pd.qs", nthreads = 10)
cao_dis <- qread("cao_oligo_astro_opc_dis.qs", nthreads = 10)Figure 4a
con.glia$plotGraph(groups = anno.glia,
plot.na = F,
size = 0.1,
palette = pal.major,
font.size = 3,
raster = T,
show.labels = T, embedding = "largeVis_CPCA_AS01",
alpha = 0.05) +
labs(x = "largeVis1", y = "largeVis2") +
theme(line = element_blank())
Figure 4b
cm.merged <- con.glia$getJointCountMatrix()
markers <- c("AQP4","TNC","MOG","LINC01608","SLC5A11","SGCZ","VCAN","OLIG2")
dotPlot(markers,
cm.merged,
anno.glia,
cols = c("white","firebrick"),
gene.order = markers)
Figure 4c
cao_msa$plotCellLoadings(show.pvals = F,
alpha = 0)$data %>%
ggplot(aes(ind, values, fill = ind)) +
geom_hline(yintercept = 0, col = "black") +
geom_violin() +
coord_flip() +
theme_bw() +
scale_fill_manual(values = pal.major) +
theme(legend.position = "none",
line = element_blank()) +
scale_y_continuous(breaks = c(-1,0,1),
limits = c(-1,1),
labels = c("-1\nCTRL",0,"1\nMSA")) +
geom_vline(xintercept = 6.5, col = "red") +
labs(x = "", y = "")## Warning: Removed 13 rows containing non-finite outside the scale range
## (`stat_ydensity()`).
Figure 4d
cao_pd$plotCellLoadings(show.pvals = F,
alpha = 0)$data %>%
ggplot(aes(ind, values, fill = ind)) +
geom_hline(yintercept = 0, col = "black") +
geom_violin() +
coord_flip() +
theme_bw() +
scale_fill_manual(values = pal.major) +
theme(legend.position = "none",
line = element_blank()) +
scale_y_continuous(breaks = c(-1,0,1),
limits = c(-1,1),
labels = c("-1\nCTRL",0,"1\nPD")) +
geom_vline(xintercept = 4.5, col = "red") +
labs(x = "", y = "")## Warning: Removed 59 rows containing non-finite outside the scale range
## (`stat_ydensity()`).
Figure 4e
cao_dis$plotCellLoadings(show.pvals = F,
alpha = 0)$data %>%
ggplot(aes(ind, values, fill = ind)) +
geom_hline(yintercept = 0, col = "black") +
geom_violin() +
coord_flip() +
theme_bw() +
scale_fill_manual(values = pal.major) +
theme(legend.position = "none",
line = element_blank()) +
scale_y_continuous(breaks = c(-1,0,1),
limits = c(-1,1),
labels = c("-1\nPD",0,"1\nMSA")) +
geom_vline(xintercept = 6.5, col = "red") +
labs(x = "", y = "")## Warning: Removed 10 rows containing non-finite outside the scale range
## (`stat_ydensity()`).
Figure 4f
cao_msa$plotExpressionShiftMagnitudes() + labs(y = "Normalized expression distance")
cao_pd$plotExpressionShiftMagnitudes() + labs(y = "Normalized expression distance")
cao_dis$plotExpressionShiftMagnitudes() + labs(y = "Normalized expression distance")
Figure 4h,i
cao_msa <- qread("cao_astro_msa.qs", nthreads = 10)
cao_pd <- qread("cao_astro_pd.qs", nthreads = 10)
cao_dis <- qread("cao_astro_dis.qs", nthreads = 10)
df.all <- Map(getOntWithFamily, list(cao_msa, cao_pd, cao_dis), c("CTRL vs. MSA", "CTRL vs. PD", "PD vs. MSA")) %>%
lget("df.all") %>%
bind_rows() %>%
mutate(Group = paste0("AS_", strsplit(Group, "_") %>% sget(1) %>% tolower()))## Loading required package: DOSE## ## DOSE v3.30.5 For help: https://yulab-smu.top/biomedical-knowledge-mining-book/
##
## If you use DOSE in published research, please cite:
## Guangchuang Yu, Li-Gen Wang, Guang-Rong Yan, Qing-Yu He. DOSE: an R/Bioconductor package for Disease Ontology Semantic and Enrichment analysis. Bioinformatics 2015, 31(4):608-609# Select relevant pathways
p.ids <- c("GO:0050821", "GO:0050728", "GO:0006986", "GO:0030168", "GO:0030001", "GO:1904707", "GO:0140053", "GO:0006486", "GO:0010717", "GO:0031113", "GO:0032675", "GO:0061041", "GO:0071222", "GO:0045766", "GO:0006487", "GO:0097501", "GO:0042776", "GO:0045047", "GO:0044794", "GO:0035633", "GO:0097242", "GO:0006120", "GO:0042026", "GO:0006979", "GO:0071222", "GO:0017015", "GO:0009611", "GO:1901201", "GO:0042594", "GO:0097501", "GO:0006123", "GO:0006909", "GO:0007179", "GO:0006120", "GO:0097501")Figure 4h
ct <- "AS_homeostatic"
df.sel <- df.all %>%
filter(Group == ct)
p1 <- ggplot(df.sel, aes(NES, logp, col = comp, shape = comp)) +
geom_point(data = ~filter(., p.adjust > 0.05), size = 0.1, alpha = 0.5, col = "black") +
geom_point(data = ~filter(., p.adjust <= 0.05), size = 2, alpha = 0.8) +
theme_bw() +
geom_hline(yintercept = -log10(0.05), linetype = "dashed") +
scale_color_manual(values = pal.major) +
theme(line = element_blank()) +
labs(y = "-log10(adj. p)", shape = "Comparison", col = "Comparison") +
dotSize(3)
df <- Map(getOntWithFamily, list(cao_msa, cao_pd, cao_dis), c("CTRL vs. MSA", "CTRL vs. PD", "PD vs. MSA")) %>%
lget("df") %>%
bind_rows() %>%
mutate(Group = paste0("AS_", strsplit(Group, "_") %>% sget(1) %>% tolower())) %>%
filter(Group == ct,
ID %in% p.ids)
p2 <- df %>%
filter(p.adjust <= 0.05, NES > 0) %>%
mutate(Group = factor(Group, levels = sort(unique(Group)))) %>%
select(Description, NES, Group, comp) %>%
as.data.frame() %>%
group_by(comp, Group) %>%
group_split() %>%
lapply(dplyr::slice, seq(5)) %>%
bind_rows() %>%
mutate(., x = 0, y = rev(seq(nrow(.)))) %>%
ggplot(aes(x, y, label = Description, col = comp)) +
geom_text(hjust = 0) +
theme_void() +
xlim(0, 1) +
labs(title = "Selected terms for up-regulated genes") +
scale_color_manual(values = pal.major) +
guides(col = "none")
p3 <- df %>%
filter(p.adjust <= 0.05, NES < 0) %>%
select(Description, NES, Group, comp) %>%
group_by(comp, Group) %>%
group_split() %>%
lapply(dplyr::slice, seq(5)) %>%
bind_rows() %>%
mutate(., x = 0, y = rev(seq(nrow(.)))) %>%
ggplot(aes(x, y, label = Description, col = comp)) +
geom_text(hjust = 0) +
theme_void() +
xlim(0, 1) +
labs(title = "Selected terms for down-regulated genes") +
scale_color_manual(values = pal.major) +
guides(col = "none")
plot_grid(plotlist = list(p3, p1, p2), ncol = 3, rel_widths = c(0.9, 1, 0.9))
Figure 4i
ct <- "AS_reactive"
df.sel <- df.all %>%
filter(Group == ct)
p1 <- ggplot(df.sel, aes(NES, logp, col = comp, shape = comp)) +
geom_point(data = ~filter(., p.adjust > 0.05), size = 0.1, alpha = 0.5, col = "black") +
geom_point(data = ~filter(., p.adjust <= 0.05), size = 2, alpha = 0.8) +
theme_bw() +
geom_hline(yintercept = -log10(0.05), linetype = "dashed") +
scale_color_manual(values = pal.major) +
theme(line = element_blank()) +
labs(y = "-log10(adj. p)", shape = "Comparison", col = "Comparison") +
dotSize(3)
df <- Map(getOntWithFamily, list(cao_msa, cao_pd, cao_dis), c("CTRL vs. MSA", "CTRL vs. PD", "PD vs. MSA")) %>%
lget("df") %>%
bind_rows() %>%
mutate(Group = paste0("AS_", strsplit(Group, "_") %>% sget(1) %>% tolower())) %>%
filter(Group == ct,
ID %in% p.ids)
p2 <- df %>%
filter(p.adjust <= 0.05, NES > 0) %>%
mutate(Group = factor(Group, levels = sort(unique(Group)))) %>%
select(Description, NES, Group, comp) %>%
group_by(comp, Group) %>%
group_split() %>%
lapply(dplyr::slice, seq(5)) %>%
bind_rows() %>%
mutate(., x = 0, y = rev(seq(nrow(.)))) %>%
ggplot(aes(x, y, label = Description, col = comp)) +
geom_text(hjust = 0) +
theme_void() +
xlim(0, 1) +
labs(title = "Selected terms for up-regulated genes") +
scale_color_manual(values = pal.major) +
guides(col = "none")
p3 <- df %>%
filter(p.adjust <= 0.05, NES < 0) %>%
select(Description, NES, Group, comp) %>%
group_by(comp, Group) %>%
group_split() %>%
lapply(dplyr::slice, seq(5)) %>%
bind_rows() %>%
mutate(., x = 0, y = rev(seq(nrow(.)))) %>%
ggplot(aes(x, y, label = Description, col = comp)) +
geom_text(hjust = 0) +
theme_void() +
xlim(0, 1) +
labs(title = "Selected terms for down-regulated genes") +
scale_color_manual(values = pal.major) +
guides(col = "none")
plot_grid(plotlist = list(p3, p1, p2), ncol = 3, rel_widths = c(0.9, 1, 0.9))
Figure 4j
cao_msa <- qread("cao_oligo_msa.qs", nthreads = 10)
cao_pd <- qread("cao_oligo_pd.qs", nthreads = 10)
cao_dis <- qread("cao_oligo_dis.qs", nthreads = 10)
df.all <- Map(getOntWithFamily, list(cao_msa, cao_pd, cao_dis), c("CTRL vs. MSA", "CTRL vs. PD", "PD vs. MSA")) %>%
lget("df.all") %>%
bind_rows() %>%
mutate(Group = paste0("OL_", strsplit(Group, "_") %>% sget(2) %>% gsub("SCGZ", "SGCZ", .)))
# Select relevant pathways
p.ids <- c("GO:0071345", "GO:0045596", "GO:0060271", "GO:0048709", "GO:0006986", "GO:0016032", "GO:0060271", "GO:0042026", "GO:0006986", "GO:0120034", "GO:0071346", "GO:1904262", "GO:0042776", "GO:0061077", "GO:0050821", "GO:0045047", "GO:0042776", "GO:0050821", "GO:0045047", "GO:0007042", "GO:0042776", "GO:0061077", "GO:0045047", "GO:0042776", "GO:0045047", "GO:2000249", "GO:0002474", "GO:0042026", "GO:0002040", "GO:0046330", "GO:0043542", "GO:0030036", "GO:0032981", "GO:0007042")
ct <- "OL_SLC5A11"
df.sel <- df.all %>%
filter(Group == ct)
p1 <- ggplot(df.sel, aes(NES, logp, col = comp, shape = comp)) +
geom_point(data = ~filter(., p.adjust > 0.05), size = 0.1, alpha = 0.5, col = "black") +
geom_point(data = ~filter(., p.adjust <= 0.05), size = 2, alpha = 0.8) +
theme_bw() +
geom_hline(yintercept = -log10(0.05), linetype = "dashed") +
scale_color_manual(values = pal.major) +
theme(line = element_blank()) +
labs(y = "-log10(adj. p)", shape = "Comparison", col = "Comparison") +
dotSize(3)
df <- Map(getOntWithFamily, list(cao_msa, cao_pd, cao_dis), c("CTRL vs. MSA", "CTRL vs. PD", "PD vs. MSA")) %>%
lget("df") %>%
bind_rows() %>%
mutate(Group = paste0("OL_", strsplit(Group, "_") %>% sget(2) %>% gsub("SCGZ", "SGCZ", .))) %>%
filter(Group == ct,
ID %in% p.ids)
p2 <- df %>%
filter(p.adjust <= 0.05, NES > 0) %>%
mutate(Group = factor(Group, levels = sort(unique(Group)))) %>%
select(Description, NES, Group, comp) %>%
group_by(comp, Group) %>%
group_split() %>%
lapply(dplyr::slice, seq(5)) %>%
bind_rows() %>%
mutate(., x = 0, y = rev(seq(nrow(.)))) %>%
ggplot(aes(x, y, label = Description, col = comp)) +
geom_text(hjust = 0) +
theme_void() +
xlim(0, 1) +
labs(title = "Selected terms for up-regulated genes") +
scale_color_manual(values = pal.major) +
guides(col = "none")
p3 <- df %>%
filter(p.adjust <= 0.05, NES < 0) %>%
select(Description, NES, Group, comp) %>%
group_by(comp, Group) %>%
group_split() %>%
lapply(dplyr::slice, seq(5)) %>%
bind_rows() %>%
mutate(., x = 0, y = rev(seq(nrow(.)))) %>%
ggplot(aes(x, y, label = Description, col = comp)) +
geom_text(hjust = 0) +
theme_void() +
xlim(0, 1) +
labs(title = "Selected terms for down-regulated genes") +
scale_color_manual(values = pal.major) +
guides(col = "none")
plot_grid(plotlist = list(p3, p1, p2), ncol = 3, rel_widths = c(0.9, 1, 0.9))
Figure 4k,l
cm.merged <- con$getJointCountMatrix(raw = T) %>%
Matrix::t() %>%
.[, colnames(.) %in% names(anno.glia)]
# Create sample-wise annotation
anno.donor <- con$getDatasetPerCell()[colnames(cm.merged)]
anno.subtype <- anno.glia %>%
.[!is.na(.)] %>%
factor()
idx <- intersect(anno.donor %>% names(), anno.subtype %>% names())
anno.donor %<>% .[idx]
anno.subtype %<>% .[idx] %>%
{`names<-`(as.character(.), names(.))}
anno.final <- paste0(anno.donor,"!!",anno.subtype) %>%
`names<-`(anno.donor %>% names())
# Create pseudo CM
cm.pseudo <- sccore::collapseCellsByType(cm.merged %>% Matrix::t(),
groups = anno.final, min.cell.count = 1) %>%
t() %>%
apply(2, as, "integer")
cm.pseudo %<>%
{. + 1} %>%
DESeq2::DESeqDataSetFromMatrix(.,
colnames(.) %>%
strsplit("_") %>%
sget(1) %>%
data.frame() %>%
`dimnames<-`(list(colnames(cm.pseudo), "group")),
design = ~ group) %>%
DESeq2::estimateSizeFactors() %>%
DESeq2::counts(normalized = T)## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factorsFigure 4k
# For DE between visits
genes <- "TLR1"
idx <- cm.pseudo %>%
colnames() %>%
data.frame(id = .) %>%
mutate(condition = strsplit(id, "_|!!") %>% sget(1),
ct = strsplit(id, "!!") %>% sget(2)) %>%
mutate(ord = order(condition, ct))
x <- cm.pseudo %>%
.[match(genes, rownames(.)), match(colnames(na.omit(.)), colnames(.))] %>%
.[idx$ord]
plot.dat <- x %>%
{data.frame(sample = names(.),
value = unname(.))} %>%
mutate(anno = strsplit(sample, "!!") %>%
sget(2),
condition = strsplit(sample, "!!|_") %>%
sget(1)) %>%
filter(anno %in% c("OL_LINC01608", "OL_SGCZ", "OL_SLC5A11")) %>%
mutate(anno = factor(anno))
stat.test <- plot.dat %>%
group_by(anno) %>%
rstatix::wilcox_test(value ~ condition) %>%
rstatix::add_xy_position(x = "anno", step.increase = 0.1, fun = "mean_sd", scales = "free")
plot.dat %>%
ggplot(aes(anno, value)) +
geom_boxplot(aes(fill = condition)) +
theme_bw() +
theme(line = element_blank(),
axis.ticks.x = element_line(),
axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1)) +
labs(x = "", y = "Normalized pseudobulk expression", fill = "", title = "TLR1 expression") +
scale_fill_manual(values = ggsci::pal_jama()(3)) +
stat_compare_means(aes(fill = condition), method = "kruskal", label = "p.signif", label.y = 8.5e2) +
stat_pvalue_manual(stat.test)## Warning in stat_compare_means(aes(fill = condition), method = "kruskal", :
## Ignoring unknown aesthetics: fill
Figure 4l
# For DE between visits
genes <- "LRP1"
idx <- cm.pseudo %>%
colnames() %>%
data.frame(id = .) %>%
mutate(condition = strsplit(id, "_|!!") %>% sget(1),
ct = strsplit(id, "!!") %>% sget(2)) %>%
mutate(ord = order(condition, ct))
x <- cm.pseudo %>%
.[match(genes, rownames(.)), match(colnames(na.omit(.)), colnames(.))] %>%
.[idx$ord]
plot.dat <- x %>%
{data.frame(sample = names(.),
value = unname(.))} %>%
mutate(anno = strsplit(sample, "!!") %>%
sget(2),
condition = strsplit(sample, "!!|_") %>%
sget(1)) %>%
filter(anno %in% c("OL_LINC01608", "OL_SGCZ", "OL_SLC5A11")) %>%
mutate(anno = factor(anno))
stat.test <- plot.dat %>%
group_by(anno) %>%
rstatix::wilcox_test(value ~ condition) %>%
rstatix::add_xy_position(x = "anno", step.increase = 0.1)
plot.dat %>%
ggplot(aes(anno, value)) +
geom_boxplot(aes(fill = condition)) +
theme_bw() +
theme(line = element_blank(),
axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1),
axis.ticks.x = element_line()) +
labs(x = "", y = "Normalized pseudobulk expression", fill = "", title = "LRP1 expression") +
scale_fill_manual(values = ggsci::pal_jama()(3)) +
stat_compare_means(aes(fill = condition), method = "kruskal", label = "p.signif", label.y = 175) +
stat_pvalue_manual(stat.test) +
ylim(c(0, 180))## Warning in stat_compare_means(aes(fill = condition), method = "kruskal", :
## Ignoring unknown aesthetics: fill## Warning: Removed 1 row containing non-finite outside the scale range
## (`stat_bracket()`).
Figure 4m
For calculation of data, see Liana.ipynb.
dat <- read.delim("liana_res.csv",
sep = ",",
header = T) %>%
mutate(group = strsplit(sample, "_") %>%
sget(1))
dat.plot <- dat %>%
dplyr::rename(ligand = ligand_complex,
receptor = receptor_complex,
score = lrscore) %>%
filter(target == "Oligodendrocytes",
ligand %in% c("BMP1", "SERPINE2", "PSAP", "APOE", "APP", "SERPING1"),
receptor %in% c("LRP1", "BMPR1A", "LRP4")) %>%
group_by(group, ligand, receptor, source, target) %>%
summarize(score = mean(score)) %>%
ungroup() %>%
arrange(group, source, target, ligand, receptor)## `summarise()` has grouped output by 'group', 'ligand', 'receptor', 'source'.
## You can override using the `.groups` argument.dat.msa <- dat.plot %>%
filter(group == "MSA",
score > 0.39) %>%
mutate(lrst = paste0(ligand, receptor, source, target))
dat.pd <- dat.plot %>%
mutate(lrst = paste0(ligand, receptor, source, target)) %>%
filter(group == "PD",
lrst %in% dat.msa$lrst)
dat.rel <- dat.msa %>%
mutate(score = score - dat.pd$score)
dat.rel %>%
lianaCircos()
Figure 5 & EDF7
Load data
cao <- qread("cao_micro_pvm.qs", nthreads = 10)
cao_msa <- qread("cao_micro_pvm_msa.qs", nthreads = 10)
cao_pd <- qread("cao_micro_pvm_pd.qs", nthreads = 10)
cao_dis <- qread("cao_micro_pvm_dis.qs", nthreads = 10)sample.groups <- cao$sample.groupsFigure 5a
cao$plotEmbedding(groups = anno.micro,
size = 0.5,
palette = pal.major,
font.size = 3,
raster = T,
mark.groups = T,
plot.na = F,
alpha = 0.2) +
labs(x="UMAP1", y= "UMAP2") +
theme(line = element_blank())
Figure 5b
cm.merged <- cao$data.object$getJointCountMatrix()
markers <- c("AIF1","F13A1","MRC1","CD163","CD74")
dotPlot(markers,
cm.merged,
cao$cell.groups,
cols = c("white","firebrick"))
Figure 5d-f
anno.sort <- anno.micro[rownames(cao$data.object$embedding)]
anno.sel <- anno.sort[!anno.sort %in% c("MIC_intermediate1", "PVMs")] %>% factor()
anno.sel2 <- anno.sort[!anno.sort %in% c("MIC_intermediate2", "MIC_activated")] %>% factor()
ldata1 <- getTscanTrajectory(cao$data.object, anno.sel)
ldata2 <- getTscanTrajectory(cao$data.object, anno.sel2)
ldata <- rbind(ldata1, ldata2)Figure 5d
cao$data.object$embedding %>%
as.data.frame() %>%
mutate(., unname(anno.micro[rownames(.)])) %>%
setNames(c("UMAP1", "UMAP2", "annotation")) %>%
ggplot() +
geom_point(aes(UMAP1, UMAP2, col = annotation), size = 0.3) +
geom_line(data = ldata, mapping=aes(UMAP1, UMAP2, group = edge), linewidth = 1) +
theme_bw() +
theme(legend.position = "right",
line = element_blank()) +
labs(col = "", x = "UMAP1", y = "UMAP2") +
scale_colour_manual(values = pal.major) +
dotSize(3)
Figures 5e,f & EDF7
Prepare data
emb <- cao$data.object$embedding %>%
`colnames<-`(c("UMAP1","UMAP2")) %>%
.[rownames(.) %in% names(anno.sel2), ]
sds_obj <- slingshot(emb,
anno.sel2,
start.clus = "MIC_steady-state",
stretch = 0
)
sds <- as.SlingshotDataSet(sds_obj)
pseudotime <- sds_obj@assays@data@listData$pseudotime[, 1]Here, we show the code for running the generalized additive mixed
model. It takes a significant amount of time to run, we provide data in
pseudotime_micro_l2.qs.
mat <- cao$data.object$getJointCountMatrix() %>%
.[match(names(sds_obj), rownames(.)), colSums(.) > 2e2] %>%
.[, !grepl(pattern = "RPL|RPS|MT-", colnames(.))]
mt <- mat %>%
as.matrix() %>%
as.data.frame() %>%
tibble::rownames_to_column() %>%
{message("Mutating"); mutate(.,
pseudotime = pseudotime[rowname],
condition = cao$data.object$getDatasetPerCell()[rowname] %>%
as.character() %>%
strsplit("_") %>%
sget(1),
sample = strsplit(rowname, "!!") %>%
sget(1))} %>%
.[complete.cases(.), ] %>%
{message("Melting"); reshape2::melt(., id.vars = c("rowname", "pseudotime", "condition", "sample"))} %$%
{message("Splitting"); split(., variable)}library(gamm4)
res <- mt %>%
sccore::plapply(\(x) {
fit_full <- gamm4(data = x, formula = value ~ s(pseudotime, by = factor(condition)), random = ~(1 | sample), REML = F)
fit_reduced <- gamm4(data = x, formula = value ~ s(pseudotime), random = ~(1 | sample), REML = F)
fit_none <- gamm4(data = x, formula = value ~ 1, random = ~(1 | sample), REML = F)
ann <- anova(fit_full$mer, fit_reduced$mer, fit_none$mer)
if (any(ann$`Pr(>Chisq)` <= 0.05, na.rm = T)) {
residuals <- predict(fit_full$gam, se.fit = T)$fit %>%
unname()
r.sq <- c(summary(fit_full$gam)$r.sq, summary(fit_reduced$gam)$r.sq) %>%
setNames(c("full", "reduced"))
out <- list(anova = ann,
residuals = residuals,
r.sq = r.sq)
return(out)
}
}, n.cores = 40, mc.preschedule = T, mc.cleanup = T, progress = F) %>%
.[!sapply(., is.null)]
qsave(res, "pseudotime_micro_l2.qs")Figure 5e
cao$data.object$embedding %>%
as.data.frame() %>%
mutate(., unname(anno.micro[rownames(.)])) %>%
setNames(c("UMAP1", "UMAP2", "annotation")) %>%
mutate(., pseudotime = pseudotime[rownames(.)]) %>%
ggplot() +
geom_point(aes(UMAP1, UMAP2, col = pseudotime), size = 0.3) +
geom_line(data = ldata2, aes(UMAP1, UMAP2, group = edge), size = 1) +
theme_bw() +
theme(line = element_blank()) +
scale_color_gradient(low = "navyblue", high = "orange") +
labs(col = "Pseudotime", x = "UMAP1", y = "UMAP2")## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
Figure 5f
Please note, row order may change per iteration.
res <- qread("pseudotime_micro_l2.qs")
rsq.pseudo <- res %>%
sapply(\(gene) gene$r.sq[1]) %>%
sort(decreasing = T)
res.filter <- res[names(rsq.pseudo)[seq(50)] %>% strsplit(".", fixed = T) %>% sget(1)]
cm.merged <- cao$data.object$getJointCountMatrix() %>%
.[match(names(pseudotime), rownames(.)), colnames(.) %in% names(res.filter)] %>%
.[rowSums(.) > 0, ]
## Predict smoothend expression
pseudotime.both <- pseudotime[rownames(cm.merged)]
weights.both <- sds_obj@assays@data@listData$weights %>% .[match(names(pseudotime.both), rownames(.)), 1] + 1E-7
scFit <- cm.merged %>%
Matrix::t() %>%
tradeSeq::fitGAM(pseudotime = pseudotime.both, cellWeights = weights.both, verbose = T)
Smooth <- tradeSeq::predictSmooth(scFit, gene = colnames(cm.merged), tidy = F, n=100)
# Average across replicates and scale
Smooth <- t(scale(t(Smooth)))
# Seriate the results
Smooth <- Smooth[ seriation::get_order(seriation::seriate(Smooth, method="PCA_angle")), ]
# Create heatmap
col_fun = circlize::colorRamp2(c(-4, 0, 4), c("navy", "white", "firebrick"))
Heatmap(Smooth,
col = col_fun,
cluster_columns=F,
cluster_rows=F,
show_column_names = F,
row_names_gp = grid::gpar(fontsize = 5))
Extended Data Figure 7
We provide this figure here since all data are already loaded.
cpc <- pseudotime %>%
names() %>%
strsplit("_") %>%
sget(1) %>%
factor()
res[rownames(Smooth)] %>%
lget("residuals") %>%
lapply(as.data.frame) %>%
lapply(setNames, "residuals") %>%
lapply(mutate, group = cpc, pseudotime = pseudotime) %>%
data.table::rbindlist(idcol = "gene") %>%
ggplot(aes(pseudotime, residuals, col = group)) +
geom_smooth() +
theme_bw() +
facet_wrap(~gene, ncol = 5, scales = "free")## `geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'
Figure 5g
cao_msa$plotCellLoadings(show.pvals = F,
alpha = 0)$data %>%
ggplot(aes(ind, values, fill = ind)) +
geom_hline(yintercept = 0, col = "black") +
geom_violin() +
coord_flip() +
theme_bw() +
scale_fill_manual(values = pal.major) +
theme(legend.position = "none") +
scale_y_continuous(breaks = c(-1,0,1),
limits = c(-1,1),
labels = c("-1\nCTRL",0,"1\nMSA")) +
geom_vline(xintercept = 4.5, col = "red") +
labs(x = "", y = "") +
theme(line = element_blank())
Figure 5h
cao_pd$plotCellLoadings(show.pvals = F,
alpha = 0)$data %>%
ggplot(aes(ind, values, fill = ind)) +
geom_hline(yintercept = 0, col = "black") +
geom_violin() +
coord_flip() +
theme_bw() +
scale_fill_manual(values = pal.major) +
theme(legend.position = "none") +
scale_y_continuous(breaks = c(-1,0,1),
limits = c(-1,1),
labels = c("-1\nCTRL",0,"1\nPD")) +
geom_vline(xintercept = 4.5, col = "red") +
labs(x = "", y = "") +
theme(line = element_blank())## Warning: Removed 273 rows containing non-finite outside the scale range
## (`stat_ydensity()`).
Figure 5i
cao_dis$plotCellLoadings(show.pvals = F,
alpha = 0)$data %>%
ggplot(aes(ind, values, fill = ind)) +
geom_hline(yintercept = 0, col = "black") +
geom_violin() +
coord_flip() +
theme_bw() +
scale_fill_manual(values = pal.major) +
theme(legend.position = "none") +
scale_y_continuous(breaks = c(-1,0,1),
limits = c(-1,1),
labels = c("-1\nPD",0,"1\nMSA")) +
geom_vline(xintercept = 3.5, col = "red") +
labs(x = "", y = "") +
theme(line = element_blank())## Warning: Removed 311 rows containing non-finite outside the scale range
## (`stat_ydensity()`).
Figure 5k
We provide combined data from the RNAscope experiments as a single
file res.qs.
res <- qread("RNAscope.qs") %>%
filter(target == "AIF1", Ch1NumSpots > 0) %>%
arrange(file)
area <- read.table("RNAscope_areas.tsv", sep = "\t", header = T) %>% # Million pixels
mutate(file = paste(File.name, Tissue, sep = "")) %>%
filter(file %in% res$file) %>%
arrange(file) %>%
mutate(rel_px = px.2 / 1E6)p1 <- res %>%
group_by(group, target, file) %>%
summarize(spots = mean(Ch1NumSpots)) %>%
ggplot(aes(group, spots, fill = group)) +
geom_boxplot() +
geom_jitter(width = 0.2) +
theme_bw() +
labs(y = "Mean no. spots per double-positive cell", x = "") +
stat_compare_means(method = "kruskal.test", label.y = 115) +
stat_compare_means(comparisons = comp, method = "wilcox.test") +
theme(line = element_blank()) +
guides(fill = "none") +
scale_fill_manual(values = pal.major)## `summarise()` has grouped output by 'group', 'target'. You can override using
## the `.groups` argument.p2 <- res %>%
group_by(group, target, file) %>%
summarize(no_cells = n()) %>%
as.data.frame() %>%
arrange(file) %>%
mutate(rel_cells = no_cells / area$rel_px) %>%
ggplot(aes(group, rel_cells, fill = group)) +
geom_boxplot(outliers = F) +
geom_jitter(width = 0.2) +
theme_bw() +
stat_compare_means(method = "kruskal.test", label.y = 17) +
stat_compare_means(comparisons = comp, method = "wilcox.test") +
labs(y = "No. double-positive cells\nNormalized to area", x = "") +
theme(line = element_blank()) +
guides(fill = "none") +
scale_fill_manual(values = pal.major)## `summarise()` has grouped output by 'group', 'target'. You can override using
## the `.groups` argument.plot_grid(plotlist = list(p1, p2), ncol = 1)
Figure 5l
fams <- cao_msa$test.results[["GSEA"]]$families
df.all <- list(cao_msa$.__enclos_env__$private$getOntologyPvalueResults(name = "GSEA", genes = "up", p.adj = 1, q.value = 1),
cao_msa$.__enclos_env__$private$getOntologyPvalueResults(name = "GSEA", genes = "down", p.adj = 1, q.value = 1)) %>%
bind_rows() %>%
mutate(logp = -log10(p.adjust))
# Relevant pathways
p.ids <- c("GO:0060760", "GO:0002230", "GO:0019915", "GO:1904646", "GO:0042775", "GO:0019646", "GO:0033108", "GO:0035455", "GO:0061077", "GO:0006914", "GO:0042026", "GO:0034340", "GO:0051607", "GO:0060337", "GO:0035455", "GO:0002684", "GO:0036037", "GO:0048514")
df <- cacoa:::getOntologyFamilyChildren(df.all, fams=fams, type = "GSEA", subtype = "BP") %>%
mutate(Group = paste0("MIC_", tolower(Group)) %>% gsub("MIC_pvms", "PVMs", .)) %>%
filter(ID %in% p.ids)
df.all %<>% mutate(Group = paste0("MIC_", tolower(Group)) %>% gsub("MIC_pvms", "PVMs", .))
p1 <- ggplot(df.all, aes(NES, logp, col = Group, shape = Group)) +
geom_point(data = ~filter(., p.adjust > 0.05), size = 0.1, alpha = 0.5, col = "black") +
geom_point(data = ~filter(., p.adjust <= 0.05), size = 2, alpha = 0.5) +
theme_bw() +
geom_hline(yintercept = -log10(0.05), linetype = "dashed") +
scale_color_manual(values = pal.major) +
theme(line = element_blank()) +
labs(y = "-log10(adj. p)") +
dotSize(3)
p2 <- df %>%
filter(p.adjust <= 0.05, NES > 0) %>%
mutate(Group = factor(Group, levels = sort(unique(Group)))) %>%
select(Description, NES, Group) %$%
split(., Group) %>%
lapply(dplyr::slice, seq(5)) %>%
bind_rows() %>%
dplyr::slice(seq(25)) %>%
mutate(., x = 0, y = rev(seq(nrow(.)))) %>%
ggplot(aes(x, y, label = Description, col = Group)) +
geom_text(hjust = 0) +
theme_void() +
xlim(0, 1) +
labs(title = "Selected terms for up-regulated genes") +
scale_color_manual(values = pal.major) +
guides(col = "none")
p3 <- df %>%
filter(p.adjust <= 0.05, NES < 0) %>%
select(Description, NES, Group) %>%
dplyr::slice(seq(20)) %>%
mutate(., x = 0, y = rev(seq(nrow(.)))) %>%
ggplot(aes(x, y, label = Description, col = Group)) +
geom_text(hjust = 0) +
theme_void() +
xlim(0, 1) +
labs(title = "Selected terms for down-regulated genes") +
scale_color_manual(values = pal.major) +
guides(col = "none")
plot_grid(plotlist = list(p3, p1, p2), ncol = 3, rel_widths = c(0.9, 1, 0.9))
Figure 5m
fams <- cao_pd$test.results[["GSEA"]]$families
df.all <- list(cao_pd$.__enclos_env__$private$getOntologyPvalueResults(name = "GSEA", genes = "up", p.adj = 1, q.value = 1),
cao_pd$.__enclos_env__$private$getOntologyPvalueResults(name = "GSEA", genes = "down", p.adj = 1, q.value = 1)) %>%
bind_rows() %>%
mutate(logp = -log10(p.adjust))
# Select relevant pathways
p.ids <- c("GO:0042776", "GO:0034341", "GO:0001916", "GO:0002503", "GO:0019885", "GO:0016042", "GO:0001909", "GO:0051085", "GO:0045824", "GO:0044000", "GO:0019885", "GO:0044242", "GO:0001944", "GO:0034976", "GO:0019646", "GO:0009205", "GO:0019883", "GO:0070972", "GO:0002474")
df <- cacoa:::getOntologyFamilyChildren(df.all, fams=fams, type = "GSEA", subtype = "BP") %>%
mutate(Group = paste0("MIC_", tolower(Group)) %>% gsub("MIC_pvms", "PVMs", .)) %>%
filter(ID %in% p.ids)
df.all %<>% mutate(Group = paste0("MIC_", tolower(Group)) %>% gsub("MIC_pvms", "PVMs", .))
p1 <- ggplot(df.all, aes(NES, logp, col = Group, shape = Group)) +
geom_point(data = ~filter(., p.adjust > 0.05), size = 0.1, alpha = 0.5, col = "black") +
geom_point(data = ~filter(., p.adjust <= 0.05), size = 2, alpha = 0.5) +
theme_bw() +
geom_hline(yintercept = -log10(0.05), linetype = "dashed") +
scale_color_manual(values = pal.major) +
theme(line = element_blank()) +
labs(y = "-log10(adj. p)") +
dotSize(3)
p2 <- df %>%
filter(p.adjust <= 0.05, NES > 0) %>%
select(Description, NES, Group) %$%
split(., Group) %>%
lapply(dplyr::slice, seq(5)) %>%
bind_rows() %>%
dplyr::slice(seq(25)) %>%
mutate(., x = 0, y = rev(seq(nrow(.)))) %>%
ggplot(aes(x, y, label = Description, col = Group)) +
geom_text(hjust = 0) +
theme_void() +
xlim(0, 1) +
labs(title = "Selected terms for up-regulated genes") +
scale_color_manual(values = pal.major) +
guides(col = "none")
p3 <- df %>%
filter(p.adjust <= 0.05, NES < 0) %>%
select(Description, NES, Group) %$%
split(., Group) %>%
lapply(dplyr::slice, seq(5)) %>%
bind_rows() %>%
dplyr::slice(seq(25)) %>%
mutate(., x = 0, y = rev(seq(nrow(.)))) %>%
ggplot(aes(x, y, label = Description, col = Group)) +
geom_text(hjust = 0) +
theme_void() +
xlim(0, 1) +
labs(title = "Selected terms for down-regulated genes") +
scale_color_manual(values = pal.major) +
guides(col = "none")
plot_grid(plotlist = list(p3, p1, p2), ncol = 3, rel_widths = c(0.9, 1, 0.9))
Figure 6
Figures 6c,e,f where made with GraphPad Prism. Data are available in
Phagocytosis_assay_triplicates_raw_data_HH.tsv (Copenhagen
experiment) or HOUSTON.tsv (Houston experiment).
Figure 6b
dat <- read.delim("Phagocytosis_assay_triplicates_raw_data_HH.tsv",
header = T, dec = ",") %>%
tidyr::pivot_longer(names_to = "group", cols = -1, values_to = "value") %>%
mutate(group = gsub(".1|.2", "", group) %>% factor(labels = c("PBS","LPS","MSA CSF","CTRL CSF","PD CSF"))) %>%
mutate(group = factor(group, levels = c("PBS","LPS","CTRL CSF","MSA CSF","PD CSF"))) %>%
group_by(Time, group) %>%
summarize(mean = mean(value, na.rm = T),
sd = sd(value, na.rm = T))## `summarise()` has grouped output by 'Time'. You can override using the
## `.groups` argument.dat %>%
ggplot(aes(Time, mean, group=group, color=group)) +
geom_errorbar(aes(ymin=mean-sd, ymax=mean+sd), width=.1) +
geom_point(size = 0.5) +
theme_bw() +
labs(x = "Time (hours)", y = "pHrodo-labelled E. coli uptake - intensity ratio [AU]", col = "Stimulation") +
scale_color_manual(values = pal.major) +
theme(line = element_blank())
Figure 6d
dat.raw <- read.table("Cytokine_summary.tsv", header = TRUE, sep = "\t", dec = ",")
dat.tmp <- dat.raw %>%
melt(id.vars = c("Sample","Group","Condition")) %>%
mutate(variable = variable %>%
as.character() %>%
gsub(".", "-", ., fixed = T) %>%
as.factor()) %>%
filter(Condition == "Media", !Group %in% c("PBS","LPS")) %>%
filter(Condition == "Media") %>%
mutate(Group = Group %>% factor(levels = c("CTRL","MSA","PD")),
variable = variable %>% factor())
dat.stat <- dat.raw %>%
select(!IL.8) %>%
mutate(Group = factor(Group, levels = c("PBS","LPS","CTRL","MSA","PD"))) %>%
filter(!Group %in% c("PBS","LPS"))
res.il10 <- FSA::dunnTest(IL10 ~ Group, data = dat.stat %>% {`colnames<-`(., gsub(".", "", colnames(.), fixed = T))}, method = "bh")## Registered S3 methods overwritten by 'FSA':
## method from
## confint.boot car
## hist.boot carstat.test <- dat.tmp %>%
filter(variable == "IL-10") %>%
select(Group, value) %>%
as.data.frame() %>%
rstatix::wilcox_test(value ~ Group) %>%
mutate(p = res.il10$res$P.unadj,
p.adj = res.il10$res$P.adj %>% formatC(digits = 2),
p.adj.signif = c("*","ns","ns")) %>%
rstatix::add_xy_position(x = "Group")
dat.tmp %>%
filter(variable == "IL-10") %>%
ggplot(aes(Group, value)) +
geom_point(aes(col = Group), size = 3, position = position_jitter(width = 0.3)) +
theme_bw() +
theme(line = element_blank()) +
stat_pvalue_manual(stat.test, label = "p.adj", tip.length = 0.01, backet.nudge.y = 3, hide.ns = F) +
scale_color_manual(values = pal.major) +
labs(x = "", y = "IL-10 pg/mL") +
guides(col = "none")
Figures 6g-i
dat.melt <- read.table("Microglia+CSF_samples_aSyn_sCD163.tsv", header = T, sep = "\t", dec = ",") %>%
melt(id.vars = c("diagnosis", "sex")) %>%
mutate(diagnosis = factor(diagnosis, levels = c("CTRL","MSA","IPD"), labels = c("CTRL","MSA","PD")))Figure 6g
dat.melt %>%
filter(variable == "sCD163_ng.mL_CSF") %>%
ggplot(aes(diagnosis, value)) +
geom_boxplot(aes(fill = diagnosis)) +
theme_bw() +
labs(x = "", y = "sCD163 ng/ml", fill = "Diagnosis") +
scale_fill_manual(values = pal.major) +
stat_compare_means(method = "t.test", comparisons = list(c("CTRL","MSA"), c("MSA","PD"), c("CTRL","PD"))) +
guides(fill = "none") +
theme(line = element_blank())
Figure 6h
dat.melt %>%
filter(variable == "aSyn_pg.mL") %>%
ggplot(aes(diagnosis, value)) +
geom_boxplot(aes(fill = diagnosis)) +
theme_bw() +
labs(x = "", y = "aSyn pg/ml", fill = "Diagnosis") +
scale_fill_manual(values = pal.major) +
stat_compare_means(method = "t.test", comparisons = list(c("CTRL","MSA"), c("MSA","PD"), c("CTRL","PD"))) +
guides(fill = "none") +
theme(line = element_blank())## Warning: Removed 36 rows containing non-finite outside the scale range
## (`stat_boxplot()`).## Warning: Removed 36 rows containing non-finite outside the scale range
## (`stat_signif()`).
Figure 6i
dat.melt %>%
filter(variable %in% c("aSyn_pg.mL","sCD163_ng.mL_CSF")) %>%
select(-sex) %>%
mutate(id = rep(seq(63), 2)) %>%
tidyr::pivot_wider(names_from = variable, values_from = value) %>%
ggplot(aes(aSyn_pg.mL, sCD163_ng.mL_CSF)) +
geom_point(aes(col = diagnosis)) +
theme_bw() +
labs(x = "aSyn pg/ml", y = "sCD163, ng/ml", col = "") +
geom_smooth(method = MASS::rlm, se = FALSE, color = "black") +
stat_poly_eq(aes(label = paste(after_stat(rr.label), after_stat(p.value.label), sep = "*\", \"*")), color = "black") +
scale_color_manual(values = pal.major) +
theme(line = element_blank())## `geom_smooth()` using formula = 'y ~ x'## Warning: Removed 36 rows containing non-finite outside the scale range
## (`stat_smooth()`).## Warning: Removed 36 rows containing non-finite outside the scale range
## (`stat_poly_eq()`).## Warning: Removed 36 rows containing missing values or values outside the scale range
## (`geom_point()`).
Extended Data Figure 1
Load data
samples <- anno.major %>%
names() %>%
strsplit("!!") %>%
sget(1) %>%
unique()
crm <- qread("crm.qs",
nthreads = 10)1a-f
mtp <- crm$selectMetrics(ids = c(1:4,6,19))
mtp %>%
lapply(\(met) crm$plotSummaryMetrics(metrics = met, comp.group = "sample", second.comp.group = "group") +
scale_fill_manual(values = pal.major) +
labs(x = "") +
theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank())) %>%
plot_grid(plotlist = ., labels = letters[1:6], ncol = 2)## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
1g
crm$plotSummaryMetrics(metrics = crm$selectMetrics(ids = 20), comp.group = "sample", second.comp.group = "group") +
scale_fill_manual(values = pal.major) +
labs(x = "") +
theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
legend.position = "right")## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
1h
crm$plotFilteredCells(doublet.method = "doubletdetection", depth.cutoff = 5e2, size = 0.2, alpha = 0.1) +
dotSize(3)## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
1i
crm$plotFilteredCells(type = "bar", doublet.method = "doubletdetection", depth.cutoff = 5e2)$data %>%
filter(sample != "MSA_1406") %>%
mutate(sample = strsplit(sample, "_") %>%
sget(1)) %$%
split(., sample) %>%
lapply(\(x) split(x, x$filter)) %>%
lapply(lapply, \(df) mutate(df, sample = paste0(sample, seq(nrow(df))))) %>%
lapply(bind_rows) %>%
bind_rows() %>%
mutate(sample = factor(sample, levels = c(paste0("CTRL", seq(10)), paste0("MSA", seq(7)), paste0("PD", seq(12))))) %>%
ggplot(aes(sample, pct, fill = filter)) +
geom_bar(stat = "identity") +
geom_text_repel(aes(label = sprintf("%0.2f", round(pct, digits = 2))),
position = position_stack(vjust = 0.5),
direction = "y",
size = 2.5) +
crm$theme +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
labs(x = "", y = "Percentage cells filtered") +
theme(legend.position = "bottom")
Extended Data Figure 2
These figures cannot be reproduced here due to GDPR. Leaving the code for visibility.
Load data
crm <- qread("crm.qs", nthreads = 10)2a
crm$plotCbCells(samples = crm$metadata$sample %>% as.character() %>% .[. != "MSA_1406"]) +
scale_x_discrete(labels = c(paste("CTRL", seq(10)), paste("MSA", seq(7)), paste("PD", seq(12)))) +
theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1))2b
crm$plotCbAmbGenes() +
theme(line = element_blank(),
axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5),
axis.ticks.x = element_line())Extended Data Figure 3
Load and prepare data
con$embedding <- con$embeddings$UMAP
sample.groups <- con$samples %>%
names() %>%
setNames(grepl.replace(., c("CTRL","MSA","PD")), .)
cao <- Cacoa$new(data.object = con,
sample.groups = ifelse(grepl("CTRL", sample.groups), "CTRL", "DISEASE") %>%
setNames(names(sample.groups)),
cell.groups = anno.major,
ref.level = "CTRL",
target.level = "DISEASE",
n.cores = 32)
meta <- read.delim("metadata.tsv", sep = "\t") %>%
filter(sample %in% names(con$samples)) %>%
tibble::column_to_rownames("sample")
cao$estimateExpressionShiftMagnitudes()## Filtering data...## Excluding cell types Exc. neurons that don't have enough samples## done!## Calculating pairwise distances using dist='cor'...## Done!cao$estimateMetadataSeparation(sample.meta = meta,
space = "expression.shifts")3a
cao$plotSampleDistances(space = "expression.shifts", show.sample.size = T, method = "UMAP")$data %>%
mutate(condition = unname(sample.groups)) %>%
ggplot(aes(x, y, shape = condition, col = condition)) +
geom_point(aes(size = n.cells)) + scale_size_continuous(trans = "log10",
range = c(5 * 0.5, 5 * 1.5), name = "Num. cells") +
theme_bw() +
scale_color_manual(values = pal.major) +
theme(axis.text = element_blank(),
axis.ticks = element_blank()) +
labs(x = "", y = "", title = "Condition", shape = "Condition", col = "Condition") +
dotSize(3)## Warning in uwot(X = X, n_neighbors = n_neighbors, n_components = n_components,
## : n_components > number of columns in input data: 2 > 1, this may give poor or
## unexpected results
3b
cao$plotSampleDistances(space = "expression.shifts", method = "UMAP", sample.colors = meta$age %>% setNames(rownames(meta)))$data %>%
mutate(condition = unname(sample.groups)) %>%
ggplot(aes(x, y, shape = condition, col = color)) +
geom_point(size = 3) +
theme_bw() +
theme(axis.text = element_blank(),
axis.ticks = element_blank()) +
labs(x = "", y = "", title = "Age", color = "Age (y)", shape = "Condition")## Warning in uwot(X = X, n_neighbors = n_neighbors, n_components = n_components,
## : n_components > number of columns in input data: 2 > 1, this may give poor or
## unexpected results
3c
cao$plotSampleDistances(space = "expression.shifts", method = "UMAP", sample.colors = meta$brain_bank %>% setNames(rownames(meta)))$data %>%
mutate(condition = unname(sample.groups)) %>%
ggplot(aes(x, y, shape = condition, col = color)) +
geom_point(size = 3) +
theme_bw() +
theme(axis.text = element_blank(),
axis.ticks = element_blank()) +
labs(x = "", y = "", title = "Brain bank", shape = "Condition", color = "Brain bank")## Warning in uwot(X = X, n_neighbors = n_neighbors, n_components = n_components,
## : n_components > number of columns in input data: 2 > 1, this may give poor or
## unexpected results
3d
cao$plotSampleDistances(space = "expression.shifts", method = "UMAP", sample.colors = meta$extract %>% setNames(rownames(meta)))$data %>%
mutate(condition = unname(sample.groups)) %>%
ggplot(aes(x, y, shape = condition, col = color)) +
geom_point(size = 3) +
theme_bw() +
theme(axis.text = element_blank(),
axis.ticks = element_blank()) +
labs(x = "", y = "", title = "Extract", shape = "Condition", color = "Extract")## Warning in uwot(X = X, n_neighbors = n_neighbors, n_components = n_components,
## : n_components > number of columns in input data: 2 > 1, this may give poor or
## unexpected results
3d
cao$plotSampleDistances(space = "expression.shifts", method = "UMAP", sample.colors = meta$flowcell %>% setNames(rownames(meta)))$data %>%
mutate(condition = unname(sample.groups)) %>%
ggplot(aes(x, y, shape = condition, col = color)) +
geom_point(size = 3) +
theme_bw() +
theme(axis.text = element_blank(),
axis.ticks = element_blank()) +
labs(x = "", y = "", title = "Flowcell", shape = "Condition", color = "Flowcell")## Warning in uwot(X = X, n_neighbors = n_neighbors, n_components = n_components,
## : n_components > number of columns in input data: 2 > 1, this may give poor or
## unexpected results
Extended Data Figure 5
cm.merged <- con$getJointCountMatrix(raw = T) %>%
Matrix::t() %>%
.[, colnames(.) %in% names(anno.major)]
# Create sample-wise annotation
anno.donor <- con$getDatasetPerCell()[colnames(cm.merged)]
anno.subtype <- anno.major %>%
.[!is.na(.)] %>%
factor()
idx <- intersect(anno.donor %>% names(), anno.subtype %>% names())
anno.donor %<>% .[idx]
anno.subtype %<>% .[idx] %>%
{`names<-`(as.character(.), names(.))}
anno.final <- paste0(anno.donor,"!!",anno.subtype) %>%
`names<-`(anno.donor %>% names())
# Create pseudo CM
cm.pseudo <- sccore::collapseCellsByType(cm.merged %>% Matrix::t(),
groups = anno.final, min.cell.count = 1) %>%
t() %>%
apply(2, as, "integer")
cm.pseudo %<>%
{. + 1} %>%
DESeq2::DESeqDataSetFromMatrix(.,
colnames(.) %>%
strsplit("_") %>%
sget(1) %>%
data.frame() %>%
`dimnames<-`(list(colnames(cm.pseudo), "group")),
design = ~ group) %>%
DESeq2::estimateSizeFactors() %>%
DESeq2::counts(normalized = T)## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factorsc("TSPO", "COQ2", "MAPT", "FBXO47", "ELOVL7", "EDN1", "GAB1", "TENM2", "RABGEF1", "PLA2G4C", "INPP4B", "ZIC1", "ZIC2", "ZIC3", "ZIC4", "SNCA", "TPPP", "TPPP") %>%
{Map(\(gene, leg) plotGenePseudoBulk(gene, cm.pseudo, leg), gene = ., leg = c(rep(F, 17), T))} %>%
cowplot::plot_grid(plotlist = ., ncol = 3)## Warning in stat_compare_means(aes(fill = condition), method = "kruskal", : Ignoring unknown aesthetics: fill
## Ignoring unknown aesthetics: fill
## Ignoring unknown aesthetics: fill
## Ignoring unknown aesthetics: fill
## Ignoring unknown aesthetics: fill
## Ignoring unknown aesthetics: fill
## Ignoring unknown aesthetics: fill
## Ignoring unknown aesthetics: fill
## Ignoring unknown aesthetics: fill
## Ignoring unknown aesthetics: fill
## Ignoring unknown aesthetics: fill
## Ignoring unknown aesthetics: fill
## Ignoring unknown aesthetics: fill
## Ignoring unknown aesthetics: fill
## Ignoring unknown aesthetics: fill
## Ignoring unknown aesthetics: fill
## Ignoring unknown aesthetics: fill
## Ignoring unknown aesthetics: fill
Extended Data Figures 6 & 8
Load and prepare data
cao <- qread("cao_micro_pvm.qs", nthreads = 10)
anno.sort <- anno.micro[rownames(cao$data.object$embedding)]
anno.sel <- anno.sort[!anno.sort %in% c("MIC_intermediate1", "PVMs")] %>% factor()
emb <- cao$data.object$embedding %>%
`colnames<-`(c("UMAP1","UMAP2")) %>%
.[rownames(.) %in% names(anno.sel), ]
sds_obj <- slingshot(emb,
anno.sel,
start.clus = "MIC_steady-state",
stretch = 0
)
sds <- as.SlingshotDataSet(sds_obj)
pseudotime <- sds_obj@assays@data@listData$pseudotime[, 1]6a
plot.df <- cao$data.object$embedding %>%
as.data.frame() %>%
.[rownames(.) %in% names(sds_obj), ] %>%
`colnames<-`(c("UMAP1","UMAP2")) %>%
mutate(., annotation = anno.micro[rownames(.)] %>% as.factor()) %>%
.[complete.cases(.),]
ldata <- getTscanTrajectory(cao$data.object, anno.sel)
cao$data.object$embedding %>%
as.data.frame() %>%
mutate(., unname(anno.micro[rownames(.)])) %>%
setNames(c("UMAP1", "UMAP2", "annotation")) %>%
mutate(., pseudotime = pseudotime[rownames(.)]) %>%
ggplot() +
geom_point(aes(UMAP1, UMAP2, col = pseudotime), size = 0.3) +
geom_line(data = ldata1, aes(UMAP1, UMAP2, group = edge), size = 1) +
theme_bw() +
theme(line = element_blank()) +
scale_color_gradient(low = "navyblue", high = "orange") +
labs(col = "Pseudotime", x = "UMAP1", y = "UMAP2")# +
6b
Here, we show the code for running the generalized additive mixed
model. It takes a significant amount of time to run, we provide data in
pseudotime_micro_l1.qs.
mat <- cao$data.object$getJointCountMatrix() %>%
.[match(names(sds_obj), rownames(.)), colSums(.) > 2e2] %>%
.[, !grepl(pattern = "RPL|RPS|MT-", colnames(.))]
mt <- mat %>%
as.matrix() %>%
as.data.frame() %>%
tibble::rownames_to_column() %>%
{message("Mutating"); mutate(.,
pseudotime = pseudotime[rowname],
condition = cao$data.object$getDatasetPerCell()[rowname] %>%
as.character() %>%
strsplit("_") %>%
sget(1),
sample = strsplit(rowname, "!!") %>%
sget(1))} %>%
.[complete.cases(.), ] %>%
{message("Melting"); reshape2::melt(., id.vars = c("rowname", "pseudotime", "condition", "sample"))} %$%
{message("Splitting"); split(., variable)}library(gamm4)
res <- mt %>%
sccore::plapply(\(x) {
fit_full <- gamm4(data = x, formula = value ~ s(pseudotime, by = factor(condition)), random = ~(1 | sample), REML = F)
fit_reduced <- gamm4(data = x, formula = value ~ s(pseudotime), random = ~(1 | sample), REML = F)
fit_none <- gamm4(data = x, formula = value ~ 1, random = ~(1 | sample), REML = F)
ann <- anova(fit_full$mer, fit_reduced$mer, fit_none$mer)
if (any(ann$`Pr(>Chisq)` <= 0.05, na.rm = T)) {
residuals <- predict(fit_full$gam, se.fit = T)$fit %>%
unname()
r.sq <- c(summary(fit_full$gam)$r.sq, summary(fit_reduced$gam)$r.sq) %>%
setNames(c("full", "reduced"))
out <- list(anova = ann,
residuals = residuals,
r.sq = r.sq)
return(out)
}
}, n.cores = 40, mc.preschedule = T, mc.cleanup = T, progress = F) %>%
.[!sapply(., is.null)]
qsave(res, "pseudotime_micro_l1.qs")res <- qread("pseudotime_micro_l1.qs")
rsq.pseudo <- res %>%
sapply(\(gene) gene$r.sq[1]) %>%
sort(decreasing = T)
res.filter <- res[names(rsq.pseudo)[seq(50)] %>% strsplit(".", fixed = T) %>% sget(1)]
cm.merged <- cao$data.object$getJointCountMatrix() %>%
.[match(names(pseudotime), rownames(.)), colnames(.) %in% names(res.filter)] %>%
.[rowSums(.) > 0, ]
## Predict smoothend expression
pseudotime.both <- pseudotime[rownames(cm.merged)]
weights.both <- sds_obj@assays@data@listData$weights %>% .[match(names(pseudotime.both), rownames(.)), 1] + 1E-7
scFit <- cm.merged %>%
Matrix::t() %>%
tradeSeq::fitGAM(pseudotime = pseudotime.both, cellWeights = weights.both, verbose = T)## Warning in max(abs(logR)): no non-missing arguments to max; returning -InfSmooth <- tradeSeq::predictSmooth(scFit, gene = colnames(cm.merged), tidy = F, n=100)
# Average across replicates and scale
Smooth <- t(scale(t(Smooth)))
# Seriate the results
Smooth <- Smooth[ seriation::get_order(seriation::seriate(Smooth, method="PCA_angle")), ]
# Create heatmap
col_fun = circlize::colorRamp2(c(-4, 0, 4), c("navy", "white", "firebrick"))
Heatmap(Smooth,
col = col_fun,
cluster_columns=F,
cluster_rows=F,
show_column_names = F,
row_names_gp = grid::gpar(fontsize = 5))
8
cpc <- pseudotime %>%
names() %>%
strsplit("_") %>%
sget(1) %>%
factor()
res.filter %>%
lget("residuals") %>%
lapply(as.data.frame) %>%
lapply(setNames, "residuals") %>%
lapply(mutate, group = cpc, pseudotime = pseudotime) %>%
data.table::rbindlist(idcol = "gene") %>%
ggplot(aes(pseudotime, residuals, col = group)) +
geom_smooth() +
theme_bw() +
facet_wrap(~gene, ncol = 5, scales = "free")## `geom_smooth()` using method = 'gam' and formula = 'y ~ s(x, bs = "cs")'
Extended Data Figure 9
9a
We provide the results output from scHLAcount in
scHLAcount.zip.
samples <- dir("scHLAcount/rydbirk/") %>% .[grepl(pattern = "_", .)]
cms <- lapply(samples, function(x) {
labels <- read.delim(paste0("scHLAcount/rydbirk/",x,"/labels.tsv"), header = F) %>% .[,1]
barcodes <- read.delim(paste0("scHLAcount/rydbirk/",x,"/barcodes.tsv"), header = F) %>%
.[,1] %>%
sapply(function(y) paste0(x,"one_",y))
mtx <- Matrix::readMM(paste0("scHLAcount/rydbirk/",x,"/count_matrix.mtx")) %>%
t() %>%
`dimnames<-`(list(barcodes, labels)) %>%
.[rowSums(.) > 0,] # Remove cells with no counts
tmp <- colnames(mtx) %>%
strsplit("*", T) %>%
sapply(`[[`, 1)
# If more than one allele per HLA type has been detected, sum per HLA type
if(any(tmp %>% table() %>% as.numeric() > 1)) {
cs <- colSums(mtx)
alleles <- unique(tmp)
mtx <- lapply(alleles, function(y) mtx[,tmp == y]) %>%
lapply(function(y) {
if(class(y) == "dgTMatrix") rowSums(y) else y
}) %>%
unlist() %>%
Matrix(nrow = nrow(mtx)) %>%
`rownames<-`(rownames(mtx))
}
colnames(mtx) <- unique(tmp)
return(mtx)
}) %>%
setNames(samples)Calculations
# Cell-wise
cell.counts <- sapply(cms, rowSums) %>%
unname() %>%
unlist() %>%
`names<-`(., gsub("one_","!!",names(.)))
depth <- getConosDepth(con) %>%
.[match(names(cell.counts), names(.))] %>%
{data.frame(cell = names(.),
sample = names(.) %>% strsplit("!!", T) %>% sapply(`[[`, 1),
depth = unname(.))}
depth[is.na(depth)] <- 0
cell.group <- grepl.replace(names(cell.counts), patterns = c("CTRL","PD","MSA")) %>% as.factor()
mhc1.raw <- sapply(cms, function(x) {
tmp <- x[,colnames(x) %in% c("A","B","C")]
if(class(tmp) != "numeric") rowSums(tmp) else tmp
}) %>%
unname() %>%
unlist() %>%
`names<-`(., gsub("one_","!!",names(.)))
mhc2.raw <- sapply(cms, function(x) {
tmp <- x[,!colnames(x) %in% c("A","B","C")]
if(class(tmp) != "numeric") rowSums(tmp) else tmp
}) %>%
unname() %>%
unlist() %>%
`names<-`(., gsub("one_","!!",names(.)))
cell.df <- data.frame(group = cell.group,
counts = cell.counts,
mhc1 = mhc1.raw,
mhc2 = mhc2.raw,
depth = depth$depth) %>%
mutate(., anno = anno.major[match(rownames(.), names(anno.major))],
sample = rownames(.) %>% sapply(strsplit, "!!", T) %>% sapply(`[[`, 1)) %>%
mutate(type = grepl.replace(anno %>% as.character(), levels(anno), c("Brain","Brain","Brain","Peripheral","Peripheral","Peripheral","Brain","Brain","Brain","Brain"))) %>%
`rownames<-`(names(cell.counts)) %>%
filter(!is.na(anno)) Load Smajic data
samples <- dir("scHLAcount/smajic") %>% .[grepl(pattern = "SRR", .)]
cms <- lapply(samples, function(x) {
labels <- read.delim(paste0("scHLAcount/smajic/",x,"/labels.tsv"), header = F) %>% .[,1]
barcodes <- read.delim(paste0("scHLAcount/smajic/",x,"/barcodes.tsv"), header = F) %>%
.[,1] %>%
sapply(function(y) paste0(x,"_",y,"_1"))
mtx <- Matrix::readMM(paste0("scHLAcount/smajic/",x,"/count_matrix.mtx")) %>%
t() %>%
`dimnames<-`(list(barcodes, labels)) %>%
.[rowSums(.) > 0,] # Remove cells with no counts
tmp <- colnames(mtx) %>%
strsplit("*", T) %>%
sapply(`[[`, 1)
# If more than one allele per HLA type has been detected, sum per HLA type
if(any(tmp %>% table() %>% as.numeric() > 1)) {
cs <- colSums(mtx)
alleles <- unique(tmp)
mtx <- lapply(alleles, function(y) mtx[,tmp == y]) %>%
lapply(function(y) {
if(class(y) == "dgTMatrix") rowSums(y) else y
}) %>%
unlist() %>%
Matrix(nrow = nrow(mtx)) %>%
`rownames<-`(rownames(mtx))
}
colnames(mtx) <- unique(tmp)
return(mtx)
}) %>%
setNames(samples)
# Correct naming
name.df <- data.frame(samples = samples, ids = c(rep("PD",6) %>% mapply(function(x,y) paste0(x,y), x = ., y = c(1,1,2:5)),
rep("CTRL",6) %>% mapply(function(x,y) paste0(x,y), x = ., y = 1:6)))
anno <- readRDS(paste0("scHLAcount/smajic/anno.sma.rds")) %>%
setNames(., names(.) %>%
strsplit("_", T) %>%
lapply(function(x) {
if(grepl("C", x[[1]])) x[[1]] %<>% gsub("C","CTRL", .)
return(x)
}) %>%
sapply(function(x) paste0(x[[1]],"_",x[[2]])))
cms <- 1:12 %>%
lapply(function(x) {
rnames <- cms[[x]] %>%
rownames() %>%
names() %>%
sapply(function(y) paste0(name.df[x,2],"_",y)) %>%
unname()
rownames(cms[[x]]) <- rnames
return(cms[[x]])
}) %>%
setNames(names(cms))Calculations
cell.df.rydbirk <- cell.df
# Cell-wise
cell.counts <- sapply(cms, rowSums) %>%
unname() %>%
unlist()
rem <- cell.counts %>%
names() %>%
table() %>%
.[. > 1] %>%
names()
cell.counts %<>% .[!names(.) %in% rem]
cell.group <- grepl.replace(names(cell.counts), patterns = c("CTRL","PD")) %>% as.factor()
mhc1.raw <- sapply(cms, function(x) {
tmp <- x[,colnames(x) %in% c("A","B","C")]
if(class(tmp) != "numeric") rowSums(tmp) else tmp
}) %>%
unname() %>%
unlist() %>%
.[!names(.) %in% rem]
mhc2.raw <- sapply(cms, function(x) {
tmp <- x[,!colnames(x) %in% c("A","B","C")]
if(class(tmp) != "numeric") rowSums(tmp) else tmp
}) %>%
unname() %>%
unlist() %>%
.[!names(.) %in% rem]
cell.df <- data.frame(group = cell.group,
counts = cell.counts,
mhc1 = mhc1.raw,
mhc2 = mhc2.raw) %>%
mutate(., anno = anno[match(rownames(.), names(anno))],
sample = rownames(.) %>% sapply(strsplit, "_", T) %>% sapply(`[[`, 1)) %>%
`rownames<-`(names(cell.counts)) %>%
filter(!is.na(anno))Integrate with our data and plot
cell.df.int <- rbind(cell.df.rydbirk %>%
select(-c("depth","type")) %>%
mutate(origin = "Rydbirk"),
cell.df %>%
mutate(anno = anno %>% factor(labels = c("Astrocytes","Exc. neurons","Inh. neurons","Endothelial","Eppendymal","Exc. neurons","Inh. neurons","Inh. neurons","Microglia","Oligodendrocytes","OPCs","Pericytes")),
origin = "Smajic")) %>%
filter(! anno %in% c("Blood_immune","Endothelial","Eppendymal","Mural","Pericytes","Pericytes/endothelial","Immune")) %>%
filter(!is.na(anno)) %>%
mutate(anno = anno %>% factor(),
origin = origin %>% factor(),
anno = anno %>% unname() %>% factor(),
sample = sample %>% unname())
cell.anno.df <-
cell.df.int %>%
group_by(anno, sample, group, origin) %>%
summarise(mhc1 = mean(mhc1),
mhc2 = mean(mhc2),
counts = mean(counts),
cells = length(sample)) %>%
as.data.frame() %>%
select(-cells, -counts, -sample)## `summarise()` has grouped output by 'anno', 'sample', 'group'. You can override
## using the `.groups` argument.p.text1 <- data.frame(signif = c("n.s.", "n.s.", "n.s.", "n.s.", "n.s.", "0.040", "0.032", "n.s."),
mhc1 = 4.5,
anno = levels(cell.anno.df$anno))
p.text2 <- data.frame(signif = c("0.00057"),
mhc2 = 8.5,
anno = " Microglia")
cowplot::plot_grid(plotlist = list(
ggplot(cell.anno.df,
aes(anno,
mhc1)) +
geom_boxplot(outlier.shape = NA,
aes(fill = group)) +
geom_point(position = position_jitterdodge(jitter.width = 0.2),
aes(fill = group,
col = origin,
shape = group),
size = 2) +
scale_color_manual(values = c("black",
"grey50")) +
labs(x = "",
y = "Mean counts per sample",
title = "MHC-I counts",
fill = "",
col = "",
shape = " ") +
geom_text(data = p.text1, aes(label = signif)) +
theme_bw() +
scale_fill_manual(values = pal.major) +
theme(legend.position = "none",
axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1),
line = element_blank()),
ggplot(cell.anno.df %>% filter(anno == "Microglia") %>% mutate(anno = " Microglia"), aes(anno, mhc2)) +
geom_boxplot(outlier.shape = NA, aes(fill = group)) +
geom_point(position = position_jitterdodge(jitter.width = 0.2), aes(fill = group, col = origin, shape = group), size = 2) +
scale_color_manual(values = c("black","grey50")) +
labs(x = "", y = "", title = "MHC-II counts", fill = "", col = "", shape = " ") +
geom_text(data = p.text2, aes(label = signif)) +
theme_bw() +
scale_fill_manual(values = pal.major) +
theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1),
line = element_blank())
), ncol = 2, rel_widths = c(2,0.9))
Kruskal-Wallis, asses differences per cell type
cell.anno.df %>%
group_by(anno) %>%
kruskal_test(mhc1 ~ group) %>%
data.frame()## anno .y. n statistic df p method
## 1 Astrocytes mhc1 38 5.4099627 2 0.0669 Kruskal-Wallis
## 2 Microglia mhc1 37 3.5900071 2 0.1660 Kruskal-Wallis
## 3 Oligodendrocytes mhc1 38 4.0883941 2 0.1290 Kruskal-Wallis
## 4 PVMs mhc1 24 3.5403947 2 0.1700 Kruskal-Wallis
## 5 OPCs mhc1 38 0.9335358 2 0.6270 Kruskal-Wallis
## 6 Inh. neurons mhc1 31 6.4335165 2 0.0401 Kruskal-Wallis
## 7 Exc. neurons mhc1 22 6.8860651 2 0.0320 Kruskal-Wallis
## 8 MSN mhc1 21 4.9345432 2 0.0848 Kruskal-Walliscell.anno.df %>%
filter(anno == "Microglia") %>%
group_by(anno) %>%
kruskal_test(mhc2 ~ group) %>%
data.frame()## anno .y. n statistic df p method
## 1 Microglia mhc2 37 14.92493 2 0.000574 Kruskal-WallisWilcoxon, assess differences between our data and Smajic per condition per cell type
cell.anno.df %>%
filter(group != "MSA",
!anno %in% c("MSN","PVMs")) %>%
group_by(anno, group) %>%
wilcox_test(mhc1 ~ origin) %>%
data.frame()## anno group .y. group1 group2 n1 n2 statistic p
## 1 Astrocytes CTRL mhc1 Rydbirk Smajic 10 6 27 0.792
## 2 Astrocytes PD mhc1 Rydbirk Smajic 11 5 26 0.913
## 3 Microglia CTRL mhc1 Rydbirk Smajic 9 6 21 0.529
## 4 Microglia PD mhc1 Rydbirk Smajic 11 5 30 0.827
## 5 Oligodendrocytes CTRL mhc1 Rydbirk Smajic 10 6 34 0.713
## 6 Oligodendrocytes PD mhc1 Rydbirk Smajic 11 5 26 0.913
## 7 OPCs CTRL mhc1 Rydbirk Smajic 10 6 15 0.118
## 8 OPCs PD mhc1 Rydbirk Smajic 11 5 31 0.743
## 9 Inh. neurons CTRL mhc1 Rydbirk Smajic 8 6 10 0.080
## 10 Inh. neurons PD mhc1 Rydbirk Smajic 7 5 23 0.432
## 11 Exc. neurons CTRL mhc1 Rydbirk Smajic 6 6 10 0.240
## 12 Exc. neurons PD mhc1 Rydbirk Smajic 3 5 5 0.549cell.anno.df %>%
filter(group != "MSA",
anno == "Microglia") %>%
group_by(anno, group) %>%
wilcox_test(mhc2 ~ origin) %>%
data.frame()## anno group .y. group1 group2 n1 n2 statistic p
## 1 Microglia CTRL mhc2 Rydbirk Smajic 9 6 19 0.368
## 2 Microglia PD mhc2 Rydbirk Smajic 11 5 40 0.1809b
We provide a curated list with MHC and cytokine genes. Please note, the order of clusters may change.
dat <- read.table("MHC_cytokines_curated.tsv", header = T, sep = "\t")
cm.merged <- con$getJointCountMatrix(raw = T) %>%
Matrix::t() %>%
.[, colnames(.) %in% names(anno.major)] %>%
.[rownames(.) %in% (dat$name[dat$class == "cytokine"] %>% gsub("-","",.)), ] %>%
.[rowSums(.) > 0,]
# Create sample-wise annotation
anno.donor <- con$getDatasetPerCell()[colnames(cm.merged)]
anno.subtype <- anno.major %>%
.[!is.na(.) & (. %in% c("Microglia", "PVMs"))] %>%
factor()
idx <- intersect(anno.donor %>% names(), anno.subtype %>% names())
anno.donor %<>% .[idx]
anno.subtype %<>% .[idx] %>%
{`names<-`(as.character(.), names(.))}
anno.final <- paste0(anno.donor,"!!",anno.subtype) %>%
`names<-`(anno.donor %>% names())
# Create pseudo CM
cm.pseudo <- sccore::collapseCellsByType(cm.merged %>% Matrix::t(),
groups = anno.final, min.cell.count = 1) %>%
t() %>%
apply(2, as, "integer")
cm.pseudo %<>%
{. + 1} %>%
DESeq2::DESeqDataSetFromMatrix(.,
colnames(.) %>%
strsplit("_") %>%
sget(1) %>%
data.frame() %>%
`dimnames<-`(list(colnames(cm.pseudo), "group")),
design = ~ group) %>%
DESeq2::estimateSizeFactors() %>%
DESeq2::counts(normalized = T)## converting counts to integer mode## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factorscm.pseudo %<>%
Matrix::t() %>%
scale() %>%
Matrix::t()
ord <- cm.pseudo %>%
colnames() %>%
{data.frame(id = .)} %>%
mutate(.,
ct = strsplit(id, "!!") %>%
sget(2),
condition = strsplit(id, "!!|_") %>%
sget(1)) %>%
arrange(ct, condition) %>%
pull(id)
cm.pseudo %<>%
.[, match(ord, colnames(.))]
cm.pseudo %<>%
as.data.frame() %>%
tibble::rownames_to_column(var = "gene") %>%
reshape2::melt(id.vars = c("gene")) %>%
mutate(variable = as.character(variable),
condition = strsplit(variable, "!!|_") %>%
sget(1),
ct = strsplit(variable, "!!") %>%
sget(2)) %>%
group_by(condition, ct, gene) %>%
summarize(m = mean(value)) %>%
ungroup() %>%
mutate(variable = paste(condition, ct, sep = "!!")) %>%
select(-condition, -ct) %>%
reshape2::dcast(gene ~ variable, value.var = "m") %>%
tibble::column_to_rownames(var = "gene")## `summarise()` has grouped output by 'condition', 'ct'. You can override using
## the `.groups` argument.tmp <- cm.pseudo %>%
colnames() %>%
strsplit("_|!!")
clusters <- tmp %>%
sget(2)
condition <- tmp %>%
sget(1)
ha <- ComplexHeatmap::HeatmapAnnotation(Annotation = clusters,
Condition = condition,
col = list(Annotation = c("Microglia" = unname(pal.major["Microglia"]),
"PVMs" = unname(pal.major["PVMs"])),
Condition = c("CTRL" = unname(pal.major["CTRL"]),
"MSA" = unname(pal.major["MSA"]),
"PD" = unname(pal.major["PD"]))
))
ComplexHeatmap::Heatmap(cm.pseudo,
name = "Expression",
show_column_names = F,
show_row_names = T,
cluster_columns = F,
show_column_dend = F,
cluster_rows = T,
top_annotation = ha,
show_row_dend = F,
column_split = colnames(cm.pseudo) %>% strsplit("!!|_") %>% sget(2) %>% unname() %>% factor(),
col=circlize::colorRamp2(c(-1, 1.2), c("white", "firebrick")),
row_km = 4)## Warning: The input is a data frame-like object, convert it to a matrix.
Extended Data Figure 10
10c
c("FOSL2", "TCF4", "STAT1", "NR3C1", "ETV7", "THRB", "BPTF", "RELB", "CEBPD", "HIF1A", "ELF1", "CREM", "ELF2", "ZNF44", "CHD1", "POU2F1", "GTF2IRD1", "NFIA", "NFE2L2", "FOXP1", "FOXO3") %>%
clusterProfiler::enrichGO("org.Hs.eg.db", "SYMBOL", "BP") %>%
enrichplot::pairwise_termsim() %>%
enrichplot::treeplot() +
scale_fill_manual(values = brewer.pal(6, "Greys")[-1])## ## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.
## ! # Invaild edge matrix for <phylo>. A <tbl_df> is returned.## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
Extended Data Figure 11
11a
We provide the results from scDRS based on Nalls et al. GWAS summary stats.
dat.scores <- qread("gwas/nalls/PD.full_score.qs")
# Load downstream analyses for cell types versus trait
dat.ct <- read.delim("gwas/nalls/downstream_celltype.tsv", header = F, sep = "\t") %$%
strsplit(V1, " ") %>%
unlist() %>%
gsub("\\", "", ., fixed = T) %>%
gsub("\n", "", ., fixed = T) %>%
.[!sapply(., \(x) x == "")] %>%
.[c(1:5,69:72, # Header
8:12,75:78, # Astro
15:19,81:84, # EN
21:25,86:89, # Immune
28:32,92:95, # IN
34:38,97:100, # MSN
40:44,102:105, # MIC
46:50,107:110, # OPCs
52:56,112:115, # OL
58:62,117:120, # PVMs
64:68,122:125 # Peri
)]
dat.sign <- dat.ct %>%
split(seq(9)) %>%
bind_rows() %>%
{`colnames<-`(.[-1, ], .[1, ])} %>%
as.data.frame()
dat.sign %<>% mutate(group = c("Astrocytes",
"Exc. neurons",
"Immune",
"Inh. neurons",
"MSN",
"Microglia",
"OPCs",
"Oligodendrocytes",
"PVMs",
"Pericytes/endothelial"))
dat.sign %<>%
filter(!group == "Unknown") %>%
mutate(p.adj = p.adjust(assoc_mcp, method = "fdr"),
hetero.adj = p.adjust(hetero_mcp, method = "fdr")) %>%
mutate(assoc_sign = sapply(assoc_mcp, \(x) if (x <= 0.001) "***" else if (x <= 0.01 && x > 0.001) "**" else if (x <= 0.05 && x > 0.01) "*" else ""),
hetero_sign = sapply(hetero_mcp, \(x) if (x <= 0.001) "###" else if (x <= 0.01 && x > 0.001) "##" else if (x <= 0.05 && x > 0.01) "#" else " ")) %>%
mutate(sign = paste0(assoc_sign,",",hetero_sign) %>% gsub(", ", "", .)) %>%
select(group, sign) %>%
dplyr::rename(type = group)
data.frame(cell = dat.scores$X,
score = dat.scores$norm_score,
type = anno.major[match(dat.scores$X,
names(anno.major))]) %>%
group_by(type) %>%
summarize(m = median(score)) %>%
filter(!is.na(type)) %>%
mutate(type = as.character(type)) %$%
arrange(., desc(m)) %>%
mutate(type = factor(type, levels = type), sign = dat.sign$sign[match(type %>% levels, dat.sign$type)]) %>%
ggplot(aes(type, m, fill = type)) +
geom_bar(stat = "identity") +
geom_text(aes(label = sign), vjust = -0.5) +
theme_bw() +
labs(y = "Mean score", x = "", title = "scDRS mean scores and associations") +
guides(fill = "none") +
theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1),
line = element_blank()) +
ylim(c(-0.6, 2)) +
scale_fill_manual(values = pal.major) +
geom_hline(yintercept = 0)
11b
dat.ct <- read.delim("gwas/nalls/downstream_celltype_microglia.tsv", header = F, sep = "\t") %$%
strsplit(V1, " ") %>%
unlist() %>%
gsub("\\", "", ., fixed = T) %>%
gsub("\n", "", ., fixed = T) %>%
gsub("group", "", .) %>%
.[!sapply(., \(x) x == "")] %>%
{c("group", .[seq(35)])} %>%
matrix(ncol = 6, byrow = T) %>%
as.data.frame() %>%
`colnames<-`(., .[1, ]) %>%
.[-1, ]
dat.sign <-
dat.ct %>%
filter(!group == "Unknown") %>%
mutate(p.adj = p.adjust(assoc_mcp, method = "fdr"),
hetero.adj = p.adjust(hetero_mcp, method = "fdr")) %>%
mutate(assoc_sign = sapply(assoc_mcp, \(x) if (x <= 0.001) "***" else if (x <= 0.01 && x > 0.001) "**" else if (x <= 0.05 && x > 0.01) "*" else ""),
hetero_sign = sapply(hetero_mcp, \(x) if (x <= 0.001) "###" else if (x <= 0.01 && x > 0.001) "##" else if (x <= 0.05 && x > 0.01) "#" else " ")) %>%
mutate(sign = paste0(assoc_sign,",",hetero_sign) %>% gsub(", ", "", .)) %>%
select(group, sign) %>%
dplyr::rename(type = group)
data.frame(cell = dat.scores$X %>% gsub("one_", "!!", .),
score = dat.scores$norm_score,
type = anno.major[match(dat.scores$X,
names(anno.major))]) %>%
filter(cell %in% names(anno.micro)) %>%
mutate(type = factor(anno.micro[.$cell])) %>%
group_by(type) %>%
summarize(m = mean(score)) %>%
filter(!is.na(type)) %>%
mutate(type = as.character(type)) %>%
arrange(desc(m)) %>%
mutate(type = factor(type, levels = type), sign = dat.sign$sign[match(type %>% levels, dat.sign$type)]) %>%
mutate(type = factor(type, levels = type)) %>%
ggplot(aes(type, m, fill = type)) +
geom_bar(stat = "identity") +
geom_text(aes(label = sign), vjust = -0.5, nudge_y = -0.05) +
theme_bw() +
theme(line = element_blank()) +
labs(y = "Mean score", x = "", title = "scDRS for microglia") +
guides(fill = "none") +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) +
ylim(c(0, 2)) +
scale_fill_manual(values = pal.major)
11c
We provide the results from scDRS based on Chia et al. GWAS summary stats.
dat.scores <- qread("gwas/chia/MSA.full_score.qs")
# Load downstream analyses for cell types versus trait
dat.ct <- read.delim("gwas/chia/downstream_celltype.tsv", header = F, sep = "\t") %$%
strsplit(V1, " ") %>%
unlist() %>%
gsub("\\", "", ., fixed = T) %>%
gsub("\n", "", ., fixed = T) %>%
.[!sapply(., \(x) x == "")] %>%
.[c(1:5,69:72, # Header
8:12,75:78, # Astro
15:19,81:84, # EN
21:25,86:89, # Immune
28:32,92:95, # IN
34:38,97:100, # MSN
40:44,102:105, # MIC
46:50,107:110, # OPCs
52:56,112:115, # OL
58:62,117:120, # PVMs
64:68,122:125 # Peri
)]
dat.sign <- dat.ct %>%
split(seq(9)) %>%
bind_rows() %>%
{`colnames<-`(.[-1, ], .[1, ])} %>%
as.data.frame()
dat.sign %<>% mutate(group = c("Astrocytes",
"Exc. neurons",
"Immune",
"Inh. neurons",
"MSN",
"Microglia",
"OPCs",
"Oligodendrocytes",
"PVMs",
"Pericytes/endothelial"))
dat.sign %<>%
filter(!group == "Unknown") %>%
mutate(assoc_sign = sapply(assoc_mcp, \(x) if (x <= 0.001) "***" else if (x <= 0.01 && x > 0.001) "**" else if (x <= 0.05 && x > 0.01) "*" else ""),
hetero_sign = sapply(hetero_mcp, \(x) if (x <= 0.001) "###" else if (x <= 0.01 && x > 0.001) "##" else if (x <= 0.05 && x > 0.01) "#" else " ")) %>%
mutate(sign = paste0(assoc_sign,",",hetero_sign) %>% gsub(",", "", .)) %>%
select(group, sign) %>%
dplyr::rename(type = group)
data.frame(cell = dat.scores$X,
score = dat.scores$norm_score,
type = anno.major[match(dat.scores$X,
names(anno.major))]) %>%
group_by(type) %>%
summarize(m = median(score)) %>%
filter(!is.na(type)) %>%
mutate(type = as.character(type)) %$%
arrange(., desc(m)) %>%
mutate(type = factor(type, levels = type), sign = dat.sign$sign[match(type %>% levels, dat.sign$type)]) %>%
ggplot(aes(type, m, fill = type)) +
geom_bar(stat = "identity") +
geom_text(aes(label = sign), vjust = 1.5) +
theme_bw() +
labs(y = "Mean score", x = "", title = "scDRS mean scores and associations") +
guides(fill = "none") +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5),
line = element_blank()) +
ylim(c(-0.5, 0.5)) +
scale_fill_manual(values = pal.major) +
geom_hline(yintercept = 0)
Extended Data Figure 12
All subfigures were made in GraphPad Prism. Data are available in
Phagocytosis_assay_triplicates_raw_data_HH.tsv (Copenhagen
experiment) or HOUSTON.tsv (Houston experiment).
Session info
Time to knit
Sys.time() - tt## Time difference of 16.95721 minssessionInfo()## R version 4.4.2 (2024-10-31)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
##
## Matrix products: default
## BLAS/LAPACK: /opt/intel/oneapi/mkl/2025.0/lib/libmkl_gf_lp64.so.2; LAPACK version 3.11.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=C
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: Europe/Berlin
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 grid stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] DOSE_3.30.5 slingshot_2.12.0
## [3] TrajectoryUtils_1.12.0 SingleCellExperiment_1.26.0
## [5] SummarizedExperiment_1.34.0 Biobase_2.64.0
## [7] GenomicRanges_1.56.1 GenomeInfoDb_1.40.1
## [9] IRanges_2.38.1 S4Vectors_0.42.1
## [11] BiocGenerics_0.50.0 MatrixGenerics_1.16.0
## [13] matrixStats_1.4.1 princurve_2.1.6
## [15] rstatix_0.7.2 stringr_1.5.1
## [17] ComplexHeatmap_2.20.0 circlize_0.4.16
## [19] ggrepel_0.9.6 CRMetrics_0.3.0
## [21] RColorBrewer_1.1-3 ggpmisc_0.6.0
## [23] ggpp_0.5.8-1 ggforce_0.4.2
## [25] reshape2_1.4.4 STRINGdb_2.16.4
## [27] ggpubr_0.6.0 cowplot_1.1.3
## [29] ggsci_3.2.0 ggplot2_3.5.1
## [31] qs_0.27.2 scHelper_0.0.4
## [33] sccore_1.0.5 cacoa_0.4.0
## [35] dplyr_1.1.4 magrittr_2.0.3
## [37] conos_1.5.2 igraph_2.0.3
## [39] Matrix_1.7-0
##
## loaded via a namespace (and not attached):
## [1] TSCAN_1.42.0 fs_1.6.4
## [3] bitops_1.0-8 enrichplot_1.24.4
## [5] httr_1.4.7 doParallel_1.0.17
## [7] tools_4.4.2 backports_1.5.0
## [9] utf8_1.2.4 R6_2.5.1
## [11] lazyeval_0.2.2 uwot_0.2.2
## [13] mgcv_1.9-1 GetoptLong_1.0.5
## [15] withr_3.0.1 gridExtra_2.3
## [17] quantreg_5.98 cli_3.6.3
## [19] Cairo_1.6-2 TSP_1.2-4
## [21] scatterpie_0.2.4 labeling_0.4.3
## [23] sass_0.4.9 pbapply_1.7-2
## [25] yulab.utils_0.1.7 gson_0.1.0
## [27] R.utils_2.12.3 plotrix_3.8-4
## [29] limma_3.60.5 rstudioapi_0.16.0
## [31] RSQLite_2.3.7 gridGraphics_0.5-1
## [33] generics_0.1.3 shape_1.4.6.1
## [35] RApiSerialize_0.1.4 combinat_0.0-8
## [37] gtools_3.9.5 car_3.1-3
## [39] GO.db_3.19.1 ggbeeswarm_0.7.2
## [41] fansi_1.0.6 abind_1.4-8
## [43] R.methodsS3_1.8.2 lifecycle_1.0.4
## [45] edgeR_4.2.1 yaml_2.3.10
## [47] carData_3.0-5 gplots_3.1.3.1
## [49] qvalue_2.36.0 SparseArray_1.4.8
## [51] Rtsne_0.17 blob_1.2.4
## [53] promises_1.3.0 crayon_1.5.3
## [55] lattice_0.22-6 KEGGREST_1.44.1
## [57] pillar_1.9.0 knitr_1.48
## [59] fgsea_1.30.0 rjson_0.2.23
## [61] codetools_0.2-20 fastmatch_1.1-4
## [63] glue_1.8.0 drat_0.2.4
## [65] ggfun_0.1.6 data.table_1.16.0
## [67] treeio_1.28.0 vctrs_0.6.5
## [69] png_0.1-8 urltools_1.7.3
## [71] gtable_0.3.5 gsubfn_0.7
## [73] cachem_1.1.0 dendsort_0.3.4
## [75] xfun_0.47 S4Arrays_1.4.1
## [77] mime_0.12 tidygraph_1.3.1
## [79] survival_3.7-0 seriation_1.5.6
## [81] iterators_1.0.14 pbmcapply_1.5.1
## [83] N2R_1.0.3 fastICA_1.2-5.1
## [85] Rook_1.2 statmod_1.5.0
## [87] brew_1.0-10 nlme_3.1-166
## [89] ggtree_3.12.0 tradeSeq_1.18.0
## [91] bit64_4.5.2 bslib_0.8.0
## [93] irlba_2.3.5.1 vipor_0.4.7
## [95] KernSmooth_2.23-24 FSA_0.9.5
## [97] colorspace_2.1-1 DBI_1.2.3
## [99] ggrastr_1.0.2 DESeq2_1.44.0
## [101] tidyselect_1.2.1 bit_4.5.0
## [103] compiler_4.4.2 chron_2.3-61
## [105] httr2_1.0.5 SparseM_1.84-2
## [107] pagoda2_1.0.12 DelayedArray_0.30.1
## [109] shadowtext_0.1.4 stringfish_0.16.0
## [111] triebeard_0.4.1 scales_1.3.0
## [113] caTools_1.18.3 rappdirs_0.3.3
## [115] digest_0.6.37 rmarkdown_2.28
## [117] ca_0.71.1 XVector_0.44.0
## [119] htmltools_0.5.8.1 pkgconfig_2.0.3
## [121] sparseMatrixStats_1.16.0 dunn.test_1.3.6
## [123] highr_0.11 fastmap_1.2.0
## [125] rlang_1.1.4 GlobalOptions_0.1.2
## [127] UCSC.utils_1.0.0 DelayedMatrixStats_1.26.0
## [129] shiny_1.9.1 farver_2.1.2
## [131] jquerylib_0.1.4 jsonlite_1.8.9
## [133] BiocParallel_1.38.0 mclust_6.1.1
## [135] GOSemSim_2.30.2 R.oo_1.26.0
## [137] confintr_1.0.2 ggplotify_0.1.2
## [139] polynom_1.4-1 Formula_1.2-5
## [141] GenomeInfoDbData_1.2.12 patchwork_1.3.0
## [143] munsell_0.5.1 Rcpp_1.0.13
## [145] ggnewscale_0.5.0 viridis_0.6.5
## [147] ape_5.8 proto_1.0.0
## [149] sqldf_0.4-11 leidenAlg_1.1.3
## [151] stringi_1.8.4 ggraph_2.2.1
## [153] zlibbioc_1.50.0 MASS_7.3-61
## [155] org.Hs.eg.db_3.19.1 plyr_1.8.9
## [157] parallel_4.4.2 graphlayouts_1.2.0
## [159] Biostrings_2.72.1 splines_4.4.2
## [161] hash_2.2.6.3 locfit_1.5-9.10
## [163] ggsignif_0.6.4 evaluate_1.0.0
## [165] RcppParallel_5.1.9 foreach_1.5.2
## [167] tweenr_2.0.3 httpuv_1.6.15
## [169] MatrixModels_0.5-3 tidyr_1.3.1
## [171] RMTstat_0.3.1 purrr_1.0.2
## [173] polyclip_1.10-7 clue_0.3-65
## [175] broom_1.0.7 xtable_1.8-4
## [177] tidytree_0.4.6 later_1.3.2
## [179] viridisLite_0.4.2 tibble_3.2.1
## [181] clusterProfiler_4.12.6 aplot_0.2.3
## [183] registry_0.5-1 memoise_2.0.1
## [185] beeswarm_0.4.0 AnnotationDbi_1.66.0
## [187] cluster_2.1.6